diff --git a/results/_include/_summary_figure.qmd b/results/_include/_summary_figure.qmd
index 38f99b46..1c3c494d 100644
--- a/results/_include/_summary_figure.qmd
+++ b/results/_include/_summary_figure.qmd
@@ -91,6 +91,7 @@ per_metric = d3.groups(results_long, d => d.method_id)
 
 resources = d3.groups(results_resources, d => d.method_id)
   .map(([method_id, values]) => {
+    // Calculate the error percentages
     const error_pct_oom = d3.mean(values, d => d.exit_code === 137)
     const error_pct_timeout = d3.mean(values, d => d.exit_code === 143)
     const error_pct_na = d3.mean(values, d => d.exit_code === 99)
@@ -100,6 +101,8 @@ resources = d3.groups(results_resources, d => d.method_id)
     const mean_disk_read_mb = mean_na_rm(values.map(d => d.disk_read_mb))
     const mean_disk_write_mb = mean_na_rm(values.map(d => d.disk_write_mb))
     const mean_duration_sec = mean_na_rm(values.map(d => d.duration_sec))
+
+    // Return the resources
     return ({
       method_id,
       error_pct_error,
diff --git a/results/batch_integration/data/dataset_info.json b/results/batch_integration/data/dataset_info.json
new file mode 100644
index 00000000..617df5db
--- /dev/null
+++ b/results/batch_integration/data/dataset_info.json
@@ -0,0 +1,62 @@
+[
+  {
+    "dataset_id": "cellxgene_census/gtex_v9",
+    "dataset_name": "GTEX v9",
+    "dataset_summary": "Single-nucleus cross-tissue molecular reference maps to decipher disease gene function",
+    "dataset_description": "Understanding the function of genes and their regulation in tissue homeostasis and disease requires knowing the cellular context in which genes are expressed in tissues across the body. Single cell genomics allows the generation of detailed cellular atlases in human tissues, but most efforts are focused on single tissue types. Here, we establish a framework for profiling multiple tissues across the human body at single-cell resolution using single nucleus RNA-Seq (snRNA-seq), and apply it to 8 diverse, archived, frozen tissue types (three donors per tissue). We apply four snRNA-seq methods to each of 25 samples from 16 donors, generating a cross-tissue atlas of 209,126 nuclei profiles, and benchmark them vs. scRNA-seq of comparable fresh tissues. We use a conditional variational autoencoder (cVAE) to integrate an atlas across tissues, donors, and laboratory methods. We highlight shared and tissue-specific features of tissue-resident immune cells, identifying tissue-restricted and non-restricted resident myeloid populations. These include a cross-tissue conserved dichotomy between LYVE1- and HLA class II-expressing macrophages, and the broad presence of LAM-like macrophages across healthy tissues that is also observed in disease. For rare, monogenic muscle diseases, we identify cell types that likely underlie the neuromuscular, metabolic, and immune components of these diseases, and biological processes involved in their pathology. For common complex diseases and traits analyzed by GWAS, we identify the cell types and gene modules that potentially underlie disease mechanisms. The experimental and analytical frameworks we describe will enable the generation of large-scale studies of how cellular and molecular processes vary across individuals and populations.",
+    "data_reference": "eraslan2022singlenucleus",
+    "data_url": "https://cellxgene.cziscience.com/collections/a3ffde6c-7ad2-498a-903c-d58e732f7470",
+    "date_created": "20-01-2025",
+    "file_size": 1016272877
+  },
+  {
+    "dataset_id": "cellxgene_census/hypomap",
+    "dataset_name": "HypoMap",
+    "dataset_summary": "A unified single cell gene expression atlas of the murine hypothalamus",
+    "dataset_description": "The hypothalamus plays a key role in coordinating fundamental body functions. Despite recent progress in single-cell technologies, a unified catalogue and molecular characterization of the heterogeneous cell types and, specifically, neuronal subtypes in this brain region are still lacking. Here we present an integrated reference atlas “HypoMap” of the murine hypothalamus consisting of 384,925 cells, with the ability to incorporate new additional experiments. We validate HypoMap by comparing data collected from SmartSeq2 and bulk RNA sequencing of selected neuronal cell types with different degrees of cellular heterogeneity.",
+    "data_reference": "steuernagel2022hypomap",
+    "data_url": "https://cellxgene.cziscience.com/collections/d86517f0-fa7e-4266-b82e-a521350d6d36",
+    "date_created": "20-01-2025",
+    "file_size": "NA"
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "dataset_name": "Diabetic Kidney Disease",
+    "dataset_summary": "Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression",
+    "dataset_description": "Multimodal single cell sequencing is a powerful tool for interrogating cell-specific changes in transcription and chromatin accessibility. We performed single nucleus RNA (snRNA-seq) and assay for transposase accessible chromatin sequencing (snATAC-seq) on human kidney cortex from donors with and without diabetic kidney disease (DKD) to identify altered signaling pathways and transcription factors associated with DKD. Both snRNA-seq and snATAC-seq had an increased proportion of VCAM1+ injured proximal tubule cells (PT_VCAM1) in DKD samples. PT_VCAM1 has a pro-inflammatory expression signature and transcription factor motif enrichment implicated NFkB signaling. We used stratified linkage disequilibrium score regression to partition heritability of kidney-function-related traits using publicly-available GWAS summary statistics. Cell-specific PT_VCAM1 peaks were enriched for heritability of chronic kidney disease (CKD), suggesting that genetic background may regulate chromatin accessibility and DKD progression. snATAC-seq found cell-specific differentially accessible regions (DAR) throughout the nephron that change accessibility in DKD and these regions were enriched for glucocorticoid receptor (GR) motifs. Changes in chromatin accessibility were associated with decreased expression of insulin receptor, increased gluconeogenesis, and decreased expression of the GR cytosolic chaperone, FKBP5, in the diabetic proximal tubule. Cleavage under targets and release using nuclease (CUT&RUN) profiling of GR binding in bulk kidney cortex and an in vitro model of the proximal tubule (RPTEC) showed that DAR co-localize with GR binding sites. CRISPRi silencing of GR response elements (GRE) in the FKBP5 gene body reduced FKBP5 expression in RPTEC, suggesting that reduced FKBP5 chromatin accessibility in DKD may alter cellular response to GR. We developed an open-source tool for single cell allele specific analysis (SALSA) to model the effect of genetic background on gene expression. Heterozygous germline single nucleotide variants (SNV) in proximal tubule ATAC peaks were associated with allele-specific chromatin accessibility and differential expression of target genes within cis-coaccessibility networks. Partitioned heritability of proximal tubule ATAC peaks with a predicted allele-specific effect was enriched for eGFR, suggesting that genetic background may modify DKD progression in a cell-specific manner.",
+    "data_reference": "wilson2022multimodal",
+    "data_url": "https://cellxgene.cziscience.com/collections/b3e2c6e3-9b05-4da9-8f42-da38a664b45b",
+    "date_created": "20-01-2025",
+    "file_size": 417716388
+  },
+  {
+    "dataset_id": "cellxgene_census/immune_cell_atlas",
+    "dataset_name": "Immune Cell Atlas",
+    "dataset_summary": "Cross-tissue immune cell analysis reveals tissue-specific features in humans",
+    "dataset_description": "Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a dataset of ~360,000 cells. To systematically resolve immune cell heterogeneity across tissues, we developed CellTypist, a machine learning tool for rapid and precise cell type annotation. Using this approach, combined with detailed curation, we determined the tissue distribution of finely phenotyped immune cell types, revealing hitherto unappreciated tissue-specific features and clonal architecture of T and B cells. Our multitissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference dataset, tissue-integrated expression analysis, and antigen receptor sequencing.",
+    "data_reference": "dominguez2022crosstissue",
+    "data_url": "https://cellxgene.cziscience.com/collections/62ef75e4-cbea-454e-a0ce-998ec40223d3",
+    "date_created": "20-01-2025",
+    "file_size": "NA"
+  },
+  {
+    "dataset_id": "cellxgene_census/mouse_pancreas_atlas",
+    "dataset_name": "Mouse Pancreatic Islet Atlas",
+    "dataset_summary": "Mouse pancreatic islet scRNA-seq atlas across sexes, ages, and stress conditions including diabetes",
+    "dataset_description": "To better understand pancreatic β-cell heterogeneity we generated a mouse pancreatic islet atlas capturing a wide range of biological conditions. The atlas contains scRNA-seq datasets of over 300,000 mouse pancreatic islet cells, of which more than 100,000 are β-cells, from nine datasets with 56 samples, including two previously unpublished datasets. The samples vary in sex, age (ranging from embryonic to aged), chemical stress, and disease status (including T1D NOD model development and two T2D models, mSTZ and db/db) together with different diabetes treatments. Additional information about data fields is available in anndata uns field 'field_descriptions' and on https://github.com/theislab/mm_pancreas_atlas_rep/blob/main/resources/cellxgene.md.",
+    "data_reference": "hrovatin2023delineating",
+    "data_url": "https://cellxgene.cziscience.com/collections/296237e2-393d-4e31-b590-b03f74ac5070",
+    "date_created": "20-01-2025",
+    "file_size": "NA"
+  },
+  {
+    "dataset_id": "cellxgene_census/tabula_sapiens",
+    "dataset_name": "Tabula Sapiens",
+    "dataset_summary": "A multiple-organ, single-cell transcriptomic atlas of humans",
+    "dataset_description": "Tabula Sapiens is a benchmark, first-draft human cell atlas of nearly 500,000 cells from 24 organs of 15 normal human subjects. This work is the product of the Tabula Sapiens Consortium. Taking the organs from the same individual controls for genetic background, age, environment, and epigenetic effects and allows detailed analysis and comparison of cell types that are shared between tissues. Our work creates a detailed portrait of cell types as well as their distribution and variation in gene expression across tissues and within the endothelial, epithelial, stromal and immune compartments.",
+    "data_reference": "consortium2022tabula",
+    "data_url": "https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5",
+    "date_created": "20-01-2025",
+    "file_size": "NA"
+  }
+]
diff --git a/results/batch_integration/data/method_info.json b/results/batch_integration/data/method_info.json
new file mode 100644
index 00000000..7f09a2a2
--- /dev/null
+++ b/results/batch_integration/data/method_info.json
@@ -0,0 +1,418 @@
+[
+  {
+    "task_id": "control_methods",
+    "method_id": "embed_cell_types",
+    "method_name": "Embed cell types",
+    "method_summary": "Cells are embedded as a one-hot encoding of celltype labels",
+    "method_description": "Cells are embedded as a one-hot encoding of celltype labels",
+    "is_baseline": true,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_batch_integration",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/control_methods/embed_cell_types:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/control_methods/embed_cell_types",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "control_methods",
+    "method_id": "embed_cell_types_jittered",
+    "method_name": "Perfect embedding by celltype with jitter",
+    "method_summary": "Cells are embedded as a one-hot encoding of celltype labels, with a small amount of random noise added to the embedding",
+    "method_description": "Cells are embedded as a one-hot encoding of celltype labels, with a small amount of random noise added to the embedding",
+    "is_baseline": true,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_batch_integration",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/control_methods/embed_cell_types_jittered:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/control_methods/embed_cell_types_jittered",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "control_methods",
+    "method_id": "no_integration",
+    "method_name": "No integration",
+    "method_summary": "Original feature space is not modified",
+    "method_description": "Original feature space is not modified",
+    "is_baseline": true,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_batch_integration",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/control_methods/no_integration:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/control_methods/no_integration",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "control_methods",
+    "method_id": "no_integration_batch",
+    "method_name": "No integration by Batch",
+    "method_summary": "Cells are embedded by computing PCA independently on each batch",
+    "method_description": "Cells are embedded by computing PCA independently on each batch",
+    "is_baseline": true,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_batch_integration",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/control_methods/no_integration_batch:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/control_methods/no_integration_batch",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "control_methods",
+    "method_id": "shuffle_integration",
+    "method_name": "Shuffle integration",
+    "method_summary": "Integrations are randomly permuted",
+    "method_description": "Integrations are randomly permuted",
+    "is_baseline": true,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_batch_integration",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/control_methods/shuffle_integration:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/control_methods/shuffle_integration",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "control_methods",
+    "method_id": "shuffle_integration_by_batch",
+    "method_name": "Shuffle integration by batch",
+    "method_summary": "Integrations are randomly permuted within each batch",
+    "method_description": "Integrations are randomly permuted within each batch",
+    "is_baseline": true,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_batch_integration",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/control_methods/shuffle_integration_by_batch:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/control_methods/shuffle_integration_by_batch",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "control_methods",
+    "method_id": "shuffle_integration_by_cell_type",
+    "method_name": "Shuffle integration by cell type",
+    "method_summary": "Integrations are randomly permuted within each cell type",
+    "method_description": "Integrations are randomly permuted within each cell type",
+    "is_baseline": true,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_batch_integration",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/control_methods/shuffle_integration_by_cell_type:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/control_methods/shuffle_integration_by_cell_type",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "batchelor_fastmnn",
+    "method_name": "batchelor fastMNN",
+    "method_summary": "Fast mutual nearest neighbors correction",
+    "method_description": "The fastMNN() approach is much simpler than the original mnnCorrect() algorithm, and proceeds in several steps.\n\n1. Perform a multi-sample PCA on the (cosine-)normalized expression values to reduce dimensionality.\n2. Identify MNN pairs in the low-dimensional space between a reference batch and a target batch.\n3. Remove variation along the average batch vector in both reference and target batches.\n4. Correct the cells in the target batch towards the reference, using locally weighted correction vectors.\n5. Merge the corrected target batch with the reference, and repeat with the next target batch.\n",
+    "is_baseline": false,
+    "references_doi": "10.1038/nbt.4091",
+    "references_bibtex": null,
+    "code_url": "https://github.com/LTLA/batchelor",
+    "documentation_url": "https://bioconductor.org/packages/batchelor/",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/batchelor_fastmnn:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/batchelor_fastmnn",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "batchelor_mnn_correct",
+    "method_name": "batchelor mnnCorrect",
+    "method_summary": "Mutual nearest neighbors correction",
+    "method_description": "Correct for batch effects in single-cell expression data using the mutual nearest neighbors method.\n",
+    "is_baseline": false,
+    "references_doi": "10.1038/nbt.4091",
+    "references_bibtex": null,
+    "code_url": "https://github.com/LTLA/batchelor",
+    "documentation_url": "https://bioconductor.org/packages/batchelor/",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/batchelor_mnn_correct:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/batchelor_mnn_correct",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "bbknn",
+    "method_name": "BBKNN",
+    "method_summary": "BBKNN creates k nearest neighbours graph by identifying neighbours within batches, then combining and processing them with UMAP for visualization.",
+    "method_description": "\"BBKNN or batch balanced k nearest neighbours graph is built for each cell by\nidentifying its k nearest neighbours within each defined batch separately,\ncreating independent neighbour sets for each cell in each batch. These sets\nare then combined and processed with the UMAP algorithm for visualisation.\"\n",
+    "is_baseline": false,
+    "references_doi": "10.1093/bioinformatics/btz625",
+    "references_bibtex": null,
+    "code_url": "https://github.com/Teichlab/bbknn",
+    "documentation_url": "https://github.com/Teichlab/bbknn#readme",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/bbknn:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/bbknn",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "combat",
+    "method_name": "Combat",
+    "method_summary": "Adjusting batch effects in microarray expression data using empirical Bayes methods",
+    "method_description": "\"An Empirical Bayes (EB) approach to correct for batch effects. It\nestimates batch-specific parameters by pooling information across genes in\neach batch and shrinks the estimates towards the overall mean of the batch\neffect estimates across all genes. These parameters are then used to adjust\nthe data for batch effects, leading to more accurate and reproducible\nresults.\"\n",
+    "is_baseline": false,
+    "references_doi": "10.1093/biostatistics/kxj037",
+    "references_bibtex": null,
+    "code_url": "https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.combat.html",
+    "documentation_url": "https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.combat.html",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/combat:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/combat",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "geneformer",
+    "method_name": "Geneformer",
+    "method_summary": "Geneformer is a foundation transformer model pretrained on a large-scale corpus of single cell transcriptomes",
+    "method_description": "Geneformer is a foundation transformer model pretrained on a large-scale\ncorpus of single cell transcriptomes to enable context-aware predictions in\nnetwork biology. For this task, Geneformer is used to create a batch-corrected\ncell embedding.\n",
+    "is_baseline": false,
+    "references_doi": ["10.1038/s41586-023-06139-9", "10.1101/2024.08.16.608180"],
+    "references_bibtex": null,
+    "code_url": "https://huggingface.co/ctheodoris/Geneformer",
+    "documentation_url": "https://geneformer.readthedocs.io/en/latest/index.html",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/geneformer:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/geneformer",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "harmony",
+    "method_name": "Harmony",
+    "method_summary": "Fast, sensitive and accurate integration of single-cell data with Harmony",
+    "method_description": "Harmony is a general-purpose R package with an efficient algorithm for integrating multiple data sets.\nIt is especially useful for large single-cell datasets such as single-cell RNA-seq.\n",
+    "is_baseline": false,
+    "references_doi": "10.1038/s41592-019-0619-0",
+    "references_bibtex": null,
+    "code_url": "https://github.com/immunogenomics/harmony",
+    "documentation_url": "https://portals.broadinstitute.org/harmony",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/harmony:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/harmony",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "harmonypy",
+    "method_name": "Harmonypy",
+    "method_summary": "harmonypy is a port of the harmony R package by Ilya Korsunsky.",
+    "method_description": "Harmony is a general-purpose R package with an efficient algorithm for integrating multiple data sets.\nIt is especially useful for large single-cell datasets such as single-cell RNA-seq.\n",
+    "is_baseline": false,
+    "references_doi": "10.1038/s41592-019-0619-0",
+    "references_bibtex": null,
+    "code_url": "https://github.com/slowkow/harmonypy",
+    "documentation_url": "https://portals.broadinstitute.org/harmony",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/harmonypy:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/harmonypy",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "liger",
+    "method_name": "LIGER",
+    "method_summary": "Linked Inference of Genomic Experimental Relationships",
+    "method_description": "LIGER or linked inference of genomic experimental relationships uses iNMF\nderiving and implementing a novel coordinate descent algorithm to efficiently\ndo the factorization. Joint clustering is performed and factor loadings are\nnormalised.\n",
+    "is_baseline": false,
+    "references_doi": "10.1016/j.cell.2019.05.006",
+    "references_bibtex": null,
+    "code_url": "https://github.com/welch-lab/liger",
+    "documentation_url": "https://github.com/welch-lab/liger",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/liger:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/liger",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "mnnpy",
+    "method_name": "mnnpy",
+    "method_summary": "Batch effect correction by matching mutual nearest neighbors, Python implementation.",
+    "method_description": "An implementation of MNN correct in python featuring low memory usage, full multicore support and compatibility with the scanpy framework.\n\nBatch effect correction by matching mutual nearest neighbors (Haghverdi et al, 2018) has been implemented as a function 'mnnCorrect' in the R package scran. Sadly it's extremely slow for big datasets and doesn't make full use of the parallel architecture of modern CPUs.\n\nThis project is a python implementation of the MNN correct algorithm which takes advantage of python's extendability and hackability. It seamlessly integrates with the scanpy framework and has multicore support in its bones.\n",
+    "is_baseline": false,
+    "references_doi": null,
+    "references_bibtex": "@misc{Kang2022,\n  author = {Kang, Chris},\n  title = {mnnpy},\n  year = {Kang2022},\n  publisher = {GitHub},\n  journal = {GitHub repository},\n  howpublished = {\\url{https://github.com/chriscainx/mnnpy}},\n  commit = {2097dec30c193f036c5ed7e1c3d1e3a6270e102b}\n}\n",
+    "code_url": "https://github.com/chriscainx/mnnpy",
+    "documentation_url": "https://github.com/chriscainx/mnnpy#readme",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/mnnpy:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/mnnpy",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "pyliger",
+    "method_name": "pyliger",
+    "method_summary": "Python implementation of LIGER (Linked Inference of Genomic Experimental Relationships",
+    "method_description": "LIGER (installed as rliger) is a package for integrating and analyzing multiple\nsingle-cell datasets, developed by the Macosko lab and maintained/extended by the\nWelch lab. It relies on integrative non-negative matrix factorization to identify\nshared and dataset-specific factors.\n",
+    "is_baseline": false,
+    "references_doi": "10.1016/j.cell.2019.05.006",
+    "references_bibtex": null,
+    "code_url": "https://github.com/welch-lab/pyliger",
+    "documentation_url": "https://github.com/welch-lab/pyliger",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/pyliger:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/pyliger",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "scalex",
+    "method_name": "SCALEX",
+    "method_summary": "Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space",
+    "method_description": "SCALEX is a method for integrating heterogeneous single-cell data online using a VAE framework. Its generalised encoder disentangles batch-related components from batch-invariant biological components, which are then projected into a common cell-embedding space.\n",
+    "is_baseline": false,
+    "references_doi": "10.1038/s41467-022-33758-z",
+    "references_bibtex": null,
+    "code_url": "https://github.com/jsxlei/SCALEX",
+    "documentation_url": "https://scalex.readthedocs.io",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/scalex:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/scalex",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "scanorama",
+    "method_name": "Scanorama",
+    "method_summary": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
+    "method_description": "Scanorama enables batch-correction and integration of heterogeneous scRNA-seq datasets.\nIt is designed to be used in scRNA-seq pipelines downstream of noise-reduction methods,\nincluding those for imputation and highly-variable gene filtering. The results from\nScanorama integration and batch correction can then be used as input to other tools\nfor scRNA-seq clustering, visualization, and analysis.\n",
+    "is_baseline": false,
+    "references_doi": "10.1038/s41587-019-0113-3",
+    "references_bibtex": null,
+    "code_url": "https://github.com/brianhie/scanorama",
+    "documentation_url": "https://github.com/brianhie/scanorama#readme",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/scanorama:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/scanorama",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "scanvi",
+    "method_name": "scANVI",
+    "method_summary": "scANVI is a deep learning method that considers cell type labels.",
+    "method_description": "scANVI (single-cell ANnotation using Variational Inference; Python class SCANVI) is a semi-supervised model for single-cell transcriptomics data. In a sense, it can be seen as a scVI extension that can leverage the cell type knowledge for a subset of the cells present in the data sets to infer the states of the rest of the cells.\n",
+    "is_baseline": false,
+    "references_doi": "10.1038/s41592-018-0229-2",
+    "references_bibtex": null,
+    "code_url": "https://github.com/scverse/scvi-tools",
+    "documentation_url": "https://docs.scvi-tools.org/en/stable/user_guide/models/scanvi.html",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/scanvi:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/scanvi",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "scgpt_finetuned",
+    "method_name": "scGPT (fine-tuned)",
+    "method_summary": "A foundation model for single-cell biology (fine-tuned)",
+    "method_description": "scGPT is a foundation model for single-cell biology based on a generative\npre-trained transformer and trained on a repository of over 33 million cells.\n\nHere, we fine-tune the pre-trained model for the batch integration task.\n",
+    "is_baseline": false,
+    "references_doi": "10.1038/s41592-024-02201-0",
+    "references_bibtex": null,
+    "code_url": "https://github.com/bowang-lab/scGPT",
+    "documentation_url": "https://scgpt.readthedocs.io/en/latest/",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/scgpt_finetuned:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/scgpt_finetuned",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "scgpt_zeroshot",
+    "method_name": "scGPT (zero shot)",
+    "method_summary": "A foundation model for single-cell biology (zero shot)",
+    "method_description": "scGPT is a foundation model for single-cell biology based on a generative\npre-trained transformer and trained on a repository of over 33 million cells.\n\nHere, we use zero-shot output from a pre-trained model to get an integrated\nembedding for the batch integration task.\n",
+    "is_baseline": false,
+    "references_doi": "10.1038/s41592-024-02201-0",
+    "references_bibtex": null,
+    "code_url": "https://github.com/bowang-lab/scGPT",
+    "documentation_url": "https://scgpt.readthedocs.io/en/latest/",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/scgpt_zeroshot:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/scgpt_zeroshot",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "scimilarity",
+    "method_name": "SCimilarity",
+    "method_summary": "SCimilarity provides unifying representation of single cell expression profiles",
+    "method_description": "SCimilarity is a unifying representation of single cell expression profiles that quantifies similarity between expression states and generalizes to represent new studies without additional training\n",
+    "is_baseline": false,
+    "references_doi": "10.1101/2023.07.18.549537",
+    "references_bibtex": null,
+    "code_url": "https://github.com/Genentech/scimilarity",
+    "documentation_url": "https://genentech.github.io/scimilarity/index.html",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/scimilarity:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/scimilarity",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "scprint",
+    "method_name": "scPRINT",
+    "method_summary": "scPRINT is a large transformer model built for the inference of gene networks",
+    "method_description": "scPRINT is a large transformer model built for the inference of gene networks\n(connections between genes explaining the cell's expression profile) from\nscRNAseq data.\n\nIt uses novel encoding and decoding of the cell expression profile and new\npre-training methodologies to learn a cell model.\n\nscPRINT can be used to perform the following analyses:\n\n- expression denoising: increase the resolution of your scRNAseq data\n- cell embedding: generate a low-dimensional representation of your dataset\n- label prediction: predict the cell type, disease, sequencer, sex, and\n  ethnicity of your cells\n- gene network inference: generate a gene network from any cell or cell\n  cluster in your scRNAseq dataset\n",
+    "is_baseline": false,
+    "references_doi": "10.1101/2024.07.29.605556",
+    "references_bibtex": null,
+    "code_url": "https://github.com/cantinilab/scPRINT",
+    "documentation_url": "https://cantinilab.github.io/scPRINT/",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/scprint:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/scprint",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "scvi",
+    "method_name": "scVI",
+    "method_summary": "scVI combines a variational autoencoder with a hierarchical Bayesian model.",
+    "method_description": "scVI combines a variational autoencoder with a hierarchical Bayesian model. It uses the negative binomial distribution to describe gene expression of each cell, conditioned on unobserved factors and the batch variable. ScVI is run as implemented in Luecken et al.\n",
+    "is_baseline": false,
+    "references_doi": "10.1038/s41592-018-0229-2",
+    "references_bibtex": null,
+    "code_url": "https://github.com/scverse/scvi-tools",
+    "documentation_url": "https://docs.scvi-tools.org/en/stable/user_guide/models/scvi.html",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/scvi:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/scvi",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  },
+  {
+    "task_id": "methods",
+    "method_id": "uce",
+    "method_name": "UCE",
+    "method_summary": "UCE offers a unified biological latent space that can represent any cell",
+    "method_description": "Universal Cell Embedding (UCE) is a single-cell foundation model that offers a\nunified biological latent space that can represent any cell, regardless of\ntissue or species\n",
+    "is_baseline": false,
+    "references_doi": "10.1101/2023.11.28.568918",
+    "references_bibtex": null,
+    "code_url": "https://github.com/snap-stanford/UCE",
+    "documentation_url": "https://github.com/snap-stanford/UCE/blob/main/README.md",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/methods/uce:build_main",
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/methods/uce",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b"
+  }
+]
diff --git a/results/batch_integration/data/metric_execution_info.json b/results/batch_integration/data/metric_execution_info.json
new file mode 100644
index 00000000..447c27f4
--- /dev/null
+++ b/results/batch_integration/data/metric_execution_info.json
@@ -0,0 +1,16676 @@
+[
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "batchelor_fastmnn",
+    "metric_component_name": "asw_batch",
+    "resources": {
+      "submit": "2025-01-20 23:51:01",
+      "exit_code": 0,
+      "duration_sec": 17.5,
+      "cpu_pct": 314.8,
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+      "disk_read_mb": 68,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "batchelor_fastmnn",
+    "metric_component_name": "asw_label",
+    "resources": {
+      "submit": "2025-01-20 23:51:01",
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+      "disk_write_mb": 1
+    }
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+    "method_id": "batchelor_fastmnn",
+    "metric_component_name": "cell_cycle_conservation",
+    "resources": {
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+    }
+  },
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+    "method_id": "batchelor_fastmnn",
+    "metric_component_name": "clustering_overlap",
+    "resources": {
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+    }
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+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "batchelor_fastmnn",
+    "metric_component_name": "graph_connectivity",
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+    "method_id": "batchelor_fastmnn",
+    "metric_component_name": "isolated_label_asw",
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+    "method_id": "batchelor_fastmnn",
+    "metric_component_name": "isolated_label_f1",
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+    "metric_component_name": "kbet",
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+    }
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+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "batchelor_fastmnn",
+    "metric_component_name": "lisi",
+    "resources": {
+      "submit": "2025-01-20 23:51:01",
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+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "batchelor_fastmnn",
+    "metric_component_name": "pcr",
+    "resources": {
+      "submit": "2025-01-20 23:51:01",
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+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "bbknn",
+    "metric_component_name": "clustering_overlap",
+    "resources": {
+      "submit": "2025-01-20 23:50:01",
+      "exit_code": "NA",
+      "duration_sec": 61.2,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
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+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "bbknn",
+    "metric_component_name": "graph_connectivity",
+    "resources": {
+      "submit": "2025-01-20 23:50:01",
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+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "bbknn",
+    "metric_component_name": "isolated_label_f1",
+    "resources": {
+      "submit": "2025-01-20 23:50:01",
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+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "bbknn",
+    "metric_component_name": "lisi",
+    "resources": {
+      "submit": "2025-01-20 23:50:01",
+      "exit_code": "NA",
+      "duration_sec": 79.4,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "combat",
+    "metric_component_name": "asw_batch",
+    "resources": {
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+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "combat",
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+    }
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+    "method_id": "combat",
+    "metric_component_name": "cell_cycle_conservation",
+    "resources": {
+      "submit": "2025-01-20 23:52:41",
+      "exit_code": 0,
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+    }
+  },
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+    "method_id": "shuffle_integration_by_cell_type",
+    "metric_component_name": "clustering_overlap",
+    "resources": {
+      "submit": "2025-01-20 23:52:21",
+      "exit_code": 0,
+      "duration_sec": 82.4,
+      "cpu_pct": 92.6,
+      "peak_memory_mb": 6554,
+      "disk_read_mb": 566,
+      "disk_write_mb": 2
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/tabula_sapiens",
+    "method_id": "shuffle_integration_by_cell_type",
+    "metric_component_name": "graph_connectivity",
+    "resources": {
+      "submit": "2025-01-20 23:52:21",
+      "exit_code": 0,
+      "duration_sec": 18.1,
+      "cpu_pct": 60.6,
+      "peak_memory_mb": 6144,
+      "disk_read_mb": 184,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/tabula_sapiens",
+    "method_id": "shuffle_integration_by_cell_type",
+    "metric_component_name": "hvg_overlap",
+    "resources": {
+      "submit": "2025-01-20 23:52:21",
+      "exit_code": 0,
+      "duration_sec": 103,
+      "cpu_pct": 107.8,
+      "peak_memory_mb": 20173,
+      "disk_read_mb": 3277,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/tabula_sapiens",
+    "method_id": "shuffle_integration_by_cell_type",
+    "metric_component_name": "isolated_label_asw",
+    "resources": {
+      "submit": "2025-01-20 23:52:21",
+      "exit_code": 0,
+      "duration_sec": 10349,
+      "cpu_pct": 3279.8,
+      "peak_memory_mb": 6656,
+      "disk_read_mb": 174,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/tabula_sapiens",
+    "method_id": "shuffle_integration_by_cell_type",
+    "metric_component_name": "isolated_label_f1",
+    "resources": {
+      "submit": "2025-01-20 23:52:21",
+      "exit_code": 0,
+      "duration_sec": 1810,
+      "cpu_pct": 99.9,
+      "peak_memory_mb": 3789,
+      "disk_read_mb": 283,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/tabula_sapiens",
+    "method_id": "shuffle_integration_by_cell_type",
+    "metric_component_name": "kbet",
+    "resources": {
+      "submit": "2025-01-20 23:52:21",
+      "exit_code": 0,
+      "duration_sec": 13567,
+      "cpu_pct": 99.7,
+      "peak_memory_mb": 52224,
+      "disk_read_mb": 185,
+      "disk_write_mb": 2
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/tabula_sapiens",
+    "method_id": "shuffle_integration_by_cell_type",
+    "metric_component_name": "lisi",
+    "resources": {
+      "submit": "2025-01-20 23:52:21",
+      "exit_code": 0,
+      "duration_sec": 4140,
+      "cpu_pct": 100,
+      "peak_memory_mb": 12698,
+      "disk_read_mb": 4096,
+      "disk_write_mb": 3688
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/tabula_sapiens",
+    "method_id": "shuffle_integration_by_cell_type",
+    "metric_component_name": "pcr",
+    "resources": {
+      "submit": "2025-01-20 23:52:21",
+      "exit_code": 0,
+      "duration_sec": 214,
+      "cpu_pct": 237,
+      "peak_memory_mb": 22426,
+      "disk_read_mb": 3072,
+      "disk_write_mb": 1
+    }
+  }
+]
diff --git a/results/batch_integration/data/metric_info.json b/results/batch_integration/data/metric_info.json
new file mode 100644
index 00000000..41455755
--- /dev/null
+++ b/results/batch_integration/data/metric_info.json
@@ -0,0 +1,197 @@
+[
+  {
+    "task_id": "metrics",
+    "component_name": "asw_batch",
+    "metric_id": "asw_batch",
+    "metric_name": "ASW batch",
+    "metric_summary": "Modified average silhouette width (ASW) of batch",
+    "metric_description": "We consider the absolute silhouette width, s(i), on\nbatch labels per cell i. Here, 0 indicates that batches are well mixed, and any\ndeviation from 0 indicates a batch effect:\n𝑠batch(𝑖)=|𝑠(𝑖)|.\n\nTo ensure higher scores indicate better batch mixing, these scores are scaled by\nsubtracting them from 1. As we expect batches to integrate within cell identity\nclusters, we compute the batchASWj score for each cell label j separately,\nusing the equation:\nbatchASW𝑗=1|𝐶𝑗|∑𝑖∈𝐶𝑗1−𝑠batch(𝑖),\n\nwhere Cj is the set of cells with the cell label j and |Cj| denotes the number of cells\nin that set.\n\nTo obtain the final batchASW score, the label-specific batchASWj scores are averaged:\nbatchASW=1|𝑀|∑𝑗∈𝑀batchASW𝑗.\n\nHere, M is the set of unique cell labels.\n",
+    "references_doi": "10.1038/s41592-021-01336-8",
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/metrics/asw_batch",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/metrics/asw_batch:build_main",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b",
+    "maximize": true
+  },
+  {
+    "task_id": "metrics",
+    "component_name": "asw_label",
+    "metric_id": "asw_label",
+    "metric_name": "ASW Label",
+    "metric_summary": "Average silhouette of cell identity labels (cell types)",
+    "metric_description": "For the bio-conservation score, the ASW was computed on cell identity labels and\nscaled to a value between 0 and 1 using the equation:\ncelltypeASW=(ASW_C+1)/2,\n\nwhere C denotes the set of all cell identity labels.\nFor information about the batch silhouette score, check sil_batch.\n",
+    "references_doi": "10.1038/s41592-021-01336-8",
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/metrics/asw_label",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/metrics/asw_label:build_main",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b",
+    "maximize": true
+  },
+  {
+    "task_id": "metrics",
+    "component_name": "cell_cycle_conservation",
+    "metric_id": "cell_cycle_conservation",
+    "metric_name": "Cell Cycle Conservation",
+    "metric_summary": "Cell cycle conservation score based on principle component regression on cell cycle gene scores",
+    "metric_description": "The cell-cycle conservation score evaluates how well the cell-cycle effect can be\ncaptured before and after integration. We computed cell-cycle scores using Scanpy's\nscore_cell_cycle function with a reference gene set from Tirosh et al for the\nrespective cell-cycle phases. We used the same set of cell-cycle genes for mouse and\nhuman data (using capitalization to convert between the gene symbols). We then computed\nthe variance contribution of the resulting S and G2/M phase scores using principal\ncomponent regression (Principal component regression), which was performed for each\nbatch separately. The differences in variance before, Varbefore, and after, Varafter,\nintegration were aggregated into a final score between 0 and 1, using the equation:\nCCconservation=1−|Varafter−Varbefore|/Varbefore.\n\nIn this equation, values close to 0 indicate lower conservation and 1 indicates complete\nconservation of the variance explained by cell cycle. In other words, the variance\nremains unchanged within each batch for complete conservation, while any deviation from\nthe preintegration variance contribution reduces the score.\n",
+    "references_doi": "10.1038/s41592-021-01336-8",
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/metrics/cell_cycle_conservation",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/metrics/cell_cycle_conservation:build_main",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b",
+    "maximize": true
+  },
+  {
+    "task_id": "metrics",
+    "component_name": "clustering_overlap",
+    "metric_id": "ari",
+    "metric_name": "ARI",
+    "metric_summary": "Adjusted Rand Index compares clustering overlap, correcting for random labels and considering correct overlaps and disagreements.",
+    "metric_description": "The Adjusted Rand Index (ARI) compares the overlap of two clusterings;\nit considers both correct clustering overlaps while also counting correct\ndisagreements between two clusterings.\nWe compared the cell-type labels with the NMI-optimized\nLouvain clustering computed on the integrated dataset.\nThe adjustment of the Rand index corrects for randomly correct labels.\nAn ARI of 0 or 1 corresponds to random labeling or a perfect match,\nrespectively.\n",
+    "references_doi": ["10.1038/s41592-021-01336-8", "10.1007/bf01908075"],
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/metrics/clustering_overlap",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/metrics/clustering_overlap:build_main",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b",
+    "maximize": true
+  },
+  {
+    "task_id": "metrics",
+    "component_name": "clustering_overlap",
+    "metric_id": "nmi",
+    "metric_name": "NMI",
+    "metric_summary": "NMI compares overlap by scaling using mean entropy terms and optimizing Louvain clustering to obtain the best match between clusters and labels.",
+    "metric_description": "Normalized Mutual Information (NMI) compares the overlap of two clusterings.\nWe used NMI to compare the cell-type labels with Louvain clusters computed on\nthe integrated dataset. The overlap was scaled using the mean of the entropy terms\nfor cell-type and cluster labels. Thus, NMI scores of 0 or 1 correspond to uncorrelated\nclustering or a perfect match, respectively. We performed optimized Louvain clustering\nfor this metric to obtain the best match between clusters and labels.\n",
+    "references_doi": ["10.1145/2808797.2809344", "10.1038/s41592-021-01336-8"],
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/metrics/clustering_overlap",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/metrics/clustering_overlap:build_main",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b",
+    "maximize": true
+  },
+  {
+    "task_id": "metrics",
+    "component_name": "graph_connectivity",
+    "metric_id": "graph_connectivity",
+    "metric_name": "Graph Connectivity",
+    "metric_summary": "Connectivity of the subgraph per cell type label",
+    "metric_description": "The graph connectivity metric assesses whether the kNN graph representation,\nG, of the integrated data directly connects all cells with the same cell\nidentity label. For each cell identity label c, we created the subset kNN\ngraph G(Nc;Ec) to contain only cells from a given label. Using these subset\nkNN graphs, we computed the graph connectivity score using the equation:\n\ngc =1/|C| Σc∈C |LCC(G(Nc;Ec))|/|Nc|.\n\nHere, C represents the set of cell identity labels, |LCC()| is the number\nof nodes in the largest connected component of the graph, and |Nc| is the\nnumber of nodes with cell identity c. The resultant score has a range\nof (0;1], where 1 indicates that all cells with the same cell identity\nare connected in the integrated kNN graph, and the lowest possible score\nindicates a graph where no cell is connected. As this score is computed\non the kNN graph, it can be used to evaluate all integration outputs.\n",
+    "references_doi": "10.1038/s41592-021-01336-8",
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/metrics/graph_connectivity",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/metrics/graph_connectivity:build_main",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b",
+    "maximize": true
+  },
+  {
+    "task_id": "metrics",
+    "component_name": "hvg_overlap",
+    "metric_id": "hvg_overlap",
+    "metric_name": "HVG overlap",
+    "metric_summary": "Overlap of highly variable genes per batch before and after integration.",
+    "metric_description": "The HVG conservation score is a proxy for the preservation of\nthe biological signal. If the data integration method returned\na corrected data matrix, we computed the number of HVGs before\nand after correction for each batch via Scanpy's\nhighly_variable_genes function (using the 'cell ranger' flavor).\nIf available, we computed 500 HVGs per batch. If fewer than 500\ngenes were present in the integrated object for a batch,\nthe number of HVGs was set to half the total genes in that batch.\nThe overlap coefficient is as follows:\noverlap(𝑋,𝑌)=|𝑋∩𝑌|/min(|𝑋|,|𝑌|),\n\nwhere X and Y denote the fraction of preserved informative genes.\nThe overall HVG score is the mean of the per-batch HVG overlap\ncoefficients.\n",
+    "references_doi": "10.1038/s41592-021-01336-8",
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/metrics/hvg_overlap",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/metrics/hvg_overlap:build_main",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b",
+    "maximize": true
+  },
+  {
+    "task_id": "metrics",
+    "component_name": "isolated_label_asw",
+    "metric_id": "isolated_label_asw",
+    "metric_name": "Isolated label ASW",
+    "metric_summary": "Evaluate how well isolated labels separate by average silhouette width",
+    "metric_description": "Isolated cell labels are defined as the labels present in the least number\nof batches in the integration task. The score evaluates how well these isolated labels\nseparate from other cell identities.\n\nThe isolated label ASW score is obtained by computing the\nASW of isolated versus non-isolated labels on the PCA embedding (ASW metric above) and\nscaling this score to be between 0 and 1. The final score for each metric version\nconsists of the mean isolated score of all isolated labels.\n",
+    "references_doi": "10.1038/s41592-021-01336-8",
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/metrics/isolated_label_asw",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/metrics/isolated_label_asw:build_main",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b",
+    "maximize": true
+  },
+  {
+    "task_id": "metrics",
+    "component_name": "isolated_label_f1",
+    "metric_id": "isolated_label_f1",
+    "metric_name": "Isolated label F1 score",
+    "metric_summary": "Evaluate how well isolated labels coincide with clusters",
+    "metric_description": "We developed two isolated label scores to evaluate how well the data integration methods\ndealt with cell identity labels shared by few batches. Specifically, we identified\nisolated cell labels as the labels present in the least number of batches in the\nintegration task.\nThe score evaluates how well these isolated labels separate from other cell identities.\nWe implemented the isolated label metric in two versions:\n(1) the best clustering of the isolated label (F1 score) and\n(2) the global ASW of the isolated label. For the cluster-based score,\nwe first optimize the cluster assignment of the isolated label using the F1 score˚\nacross louvain clustering resolutions ranging from 0.1 to 2 in resolution steps of 0.1.\nThe optimal F1 score for the isolated label is then used as the metric score.\nThe F1 score is a weighted mean of precision and recall given by the equation:\n𝐹1=2×(precision×recall)/(precision+recall).\n\nIt returns a value between 0 and 1,\nwhere 1 shows that all of the isolated label cells and no others are captured in\nthe cluster. For the isolated label ASW score, we compute the ASW of isolated\nversus nonisolated labels on the PCA embedding (ASW metric above) and scale this\nscore to be between 0 and 1. The final score for each metric version consists of\nthe mean isolated score of all isolated labels.\n",
+    "references_doi": "10.1038/s41592-021-01336-8",
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/metrics/isolated_label_f1",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/metrics/isolated_label_f1:build_main",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b",
+    "maximize": true
+  },
+  {
+    "task_id": "metrics",
+    "component_name": "kbet",
+    "metric_id": "kbet",
+    "metric_name": "kBET",
+    "metric_summary": "kBET algorithm to determine how well batches are mixed within a cell type",
+    "metric_description": "The kBET algorithm (v.0.99.6, release 4c9dafa) determines whether the label composition\nof a k nearest neighborhood of a cell is similar to the expected (global) label\ncomposition (Buettner et al., Nat Meth 2019). The test is repeated for a random subset\nof cells, and the results are summarized as a rejection rate over all tested\nneighborhoods. Thus, kBET works on a kNN graph.\n\nWe compute kNN graphs where k = 50 for joint embeddings and corrected feature outputs\nvia Scanpy preprocessing steps. To test for technical effects and to account for\ncell-type frequency shifts across datasets, we applied kBET\nseparately on the batch variable for each cell identity label. Using the kBET defaults,\na k equal to the median of the number of cells per batch within each label is used for\nthis computation. Additionally, we set the minimum and maximum thresholds of k to 10 and\n100, respectively. As kNN graphs that have been subset by cell identity labels may no\nlonger be connected, we compute kBET per connected component. If >25% of cells were\nassigned to connected components too small for kBET computation (smaller than k × 3),\nwe assigned a kBET score of 1 to denote poor batch removal. Subsequently, kBET scores\nfor each label were averaged and subtracted from 1 to give a final kBET score.\n\nIn Open Problems we do not run kBET on graph outputs to avoid computation-intensive\ndiffusion processes being run.\n",
+    "references_doi": "10.1038/s41592-021-01336-8",
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/metrics/kbet",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/metrics/kbet:build_main",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b",
+    "maximize": true
+  },
+  {
+    "task_id": "metrics",
+    "component_name": "lisi",
+    "metric_id": "ilisi",
+    "metric_name": "iLISI",
+    "metric_summary": "Local inverse Simpson's Index",
+    "metric_description": "Local Inverse Simpson's Index metrics adapted from Korsunsky et al. 2019 to run on\nall full feature, embedding and kNN integration outputs via shortest path-based\ndistance computation on single-cell kNN graphs. The metric assesses whether clusters\nof cells in a single-cell RNA-seq dataset are well-mixed across a categorical batch\nvariable.\n\nThe original LISI score ranges from 0 to the number of categories, with the latter\nindicating good cell mixing. This is rescaled to a score between 0 and 1.\n",
+    "references_doi": "10.1038/s41592-021-01336-8",
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/metrics/lisi",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/metrics/lisi:build_main",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b",
+    "maximize": true
+  },
+  {
+    "task_id": "metrics",
+    "component_name": "lisi",
+    "metric_id": "clisi",
+    "metric_name": "cLISI",
+    "metric_summary": "Local inverse Simpson's Index",
+    "metric_description": "Local Inverse Simpson's Index metrics adapted from Korsunsky et al. 2019 to run on\nall full feature, embedding and kNN integration outputs via shortest path-based\ndistance computation on single-cell kNN graphs. The metric assesses whether clusters\nof cells in a single-cell RNA-seq dataset are well-mixed across a categorical cell type variable.\n\nThe original LISI score ranges from 0 to the number of categories, with the latter indicating good cell mixing. This is rescaled to a score between 0 and 1.\n",
+    "references_doi": "10.1038/s41592-021-01336-8",
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/metrics/lisi",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/metrics/lisi:build_main",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b",
+    "maximize": true
+  },
+  {
+    "task_id": "metrics",
+    "component_name": "pcr",
+    "metric_id": "pcr",
+    "metric_name": "PCR",
+    "metric_summary": "Compare explained variance by batch before and after integration",
+    "metric_description": "Principal component regression, derived from PCA, has previously been used to quantify\nbatch removal. Briefly, the R2 was calculated from a linear regression of the\ncovariate of interest (for example, the batch variable B) onto each principal component.\nThe variance contribution of the batch effect per principal component was then\ncalculated as the product of the variance explained by the ith principal component (PC)\nand the corresponding R2(PCi|B). The sum across all variance contributions by the batch\neffects in all principal components gives the total variance explained by the batch\nvariable as follows:\nVar(𝐶|𝐵)=∑𝑖=1𝐺Var(𝐶|PC𝑖)×𝑅2(PC𝑖|𝐵),\n\nwhere Var(C|PCi) is the variance of the data matrix C explained by the ith principal\ncomponent.\n",
+    "references_doi": "10.1038/s41592-021-01336-8",
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_batch_integration/blob/0ccffac7ce92f138da631b397adc4123d514366b/src/metrics/pcr",
+    "image": "https://ghcr.io/openproblems-bio/task_batch_integration/metrics/pcr:build_main",
+    "code_version": "build_main",
+    "commit_sha": "0ccffac7ce92f138da631b397adc4123d514366b",
+    "maximize": true
+  }
+]
diff --git a/results/batch_integration/data/quality_control.json b/results/batch_integration/data/quality_control.json
new file mode 100644
index 00000000..503687c9
--- /dev/null
+++ b/results/batch_integration/data/quality_control.json
@@ -0,0 +1,7462 @@
+[
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Task info", 
+        "name": "Pct 'task_id' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing([task_info], field)", 
+        "message": "Task metadata field 'task_id' should be defined\n  Task id: task_batch_integration\n  Field: task_id\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Task info", 
+        "name": "Pct 'task_name' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing([task_info], field)", 
+        "message": "Task metadata field 'task_name' should be defined\n  Task id: task_batch_integration\n  Field: task_name\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Task info", 
+        "name": "Pct 'task_summary' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing([task_info], field)", 
+        "message": "Task metadata field 'task_summary' should be defined\n  Task id: task_batch_integration\n  Field: task_summary\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Task info", 
+        "name": "Pct 'task_description' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing([task_info], field)", 
+        "message": "Task metadata field 'task_description' should be defined\n  Task id: task_batch_integration\n  Field: task_description\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Method info", 
+        "name": "Pct 'task_id' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(method_info, field)", 
+        "message": "Method metadata field 'task_id' should be defined\n  Task id: task_batch_integration\n  Field: task_id\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Method info", 
+        "name": "Pct 'commit_sha' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(method_info, field)", 
+        "message": "Method metadata field 'commit_sha' should be defined\n  Task id: task_batch_integration\n  Field: commit_sha\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Method info", 
+        "name": "Pct 'method_id' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(method_info, field)", 
+        "message": "Method metadata field 'method_id' should be defined\n  Task id: task_batch_integration\n  Field: method_id\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Method info", 
+        "name": "Pct 'method_name' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(method_info, field)", 
+        "message": "Method metadata field 'method_name' should be defined\n  Task id: task_batch_integration\n  Field: method_name\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Method info", 
+        "name": "Pct 'method_summary' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(method_info, field)", 
+        "message": "Method metadata field 'method_summary' should be defined\n  Task id: task_batch_integration\n  Field: method_summary\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Method info", 
+        "name": "Pct 'paper_reference' missing", 
+        "value": 0.7307692307692307, 
+        "severity": 2, 
+        "severity_value": 3.0, 
+        "code": "percent_missing(method_info, field)", 
+        "message": "Method metadata field 'paper_reference' should be defined\n  Task id: task_batch_integration\n  Field: paper_reference\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Method info", 
+        "name": "Pct 'is_baseline' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(method_info, field)", 
+        "message": "Method metadata field 'is_baseline' should be defined\n  Task id: task_batch_integration\n  Field: is_baseline\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Metric info", 
+        "name": "Pct 'task_id' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(metric_info, field)", 
+        "message": "Metric metadata field 'task_id' should be defined\n  Task id: task_batch_integration\n  Field: task_id\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Metric info", 
+        "name": "Pct 'commit_sha' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(metric_info, field)", 
+        "message": "Metric metadata field 'commit_sha' should be defined\n  Task id: task_batch_integration\n  Field: commit_sha\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Metric info", 
+        "name": "Pct 'metric_id' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(metric_info, field)", 
+        "message": "Metric metadata field 'metric_id' should be defined\n  Task id: task_batch_integration\n  Field: metric_id\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Metric info", 
+        "name": "Pct 'metric_name' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(metric_info, field)", 
+        "message": "Metric metadata field 'metric_name' should be defined\n  Task id: task_batch_integration\n  Field: metric_name\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Metric info", 
+        "name": "Pct 'metric_summary' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(metric_info, field)", 
+        "message": "Metric metadata field 'metric_summary' should be defined\n  Task id: task_batch_integration\n  Field: metric_summary\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Metric info", 
+        "name": "Pct 'paper_reference' missing", 
+        "value": 1.0, 
+        "severity": 2, 
+        "severity_value": 3.0, 
+        "code": "percent_missing(metric_info, field)", 
+        "message": "Metric metadata field 'paper_reference' should be defined\n  Task id: task_batch_integration\n  Field: paper_reference\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Metric info", 
+        "name": "Pct 'maximize' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(metric_info, field)", 
+        "message": "Metric metadata field 'maximize' should be defined\n  Task id: task_batch_integration\n  Field: maximize\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Dataset info", 
+        "name": "Pct 'task_id' missing", 
+        "value": 1.0, 
+        "severity": 2, 
+        "severity_value": 3.0, 
+        "code": "percent_missing(dataset_info, field)", 
+        "message": "Dataset metadata field 'task_id' should be defined\n  Task id: task_batch_integration\n  Field: task_id\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Dataset info", 
+        "name": "Pct 'dataset_id' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(dataset_info, field)", 
+        "message": "Dataset metadata field 'dataset_id' should be defined\n  Task id: task_batch_integration\n  Field: dataset_id\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Dataset info", 
+        "name": "Pct 'dataset_name' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(dataset_info, field)", 
+        "message": "Dataset metadata field 'dataset_name' should be defined\n  Task id: task_batch_integration\n  Field: dataset_name\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Dataset info", 
+        "name": "Pct 'dataset_summary' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(dataset_info, field)", 
+        "message": "Dataset metadata field 'dataset_summary' should be defined\n  Task id: task_batch_integration\n  Field: dataset_summary\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Dataset info", 
+        "name": "Pct 'data_reference' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(dataset_info, field)", 
+        "message": "Dataset metadata field 'data_reference' should be defined\n  Task id: task_batch_integration\n  Field: data_reference\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Dataset info", 
+        "name": "Pct 'data_url' missing", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "percent_missing(dataset_info, field)", 
+        "message": "Dataset metadata field 'data_url' should be defined\n  Task id: task_batch_integration\n  Field: data_url\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw data", 
+        "name": "Number of results", 
+        "value": 156, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "len(results) == len(method_info) * len(metric_info) * len(dataset_info)", 
+        "message": "Number of results should be equal to #methods × #metrics × #datasets.\n  Task id: task_batch_integration\n  Number of results: 156\n  Number of methods: 26\n  Number of metrics: 13\n  Number of datasets: 6\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Metric 'asw_batch' %missing", 
+        "value": 0.29487179487179493, 
+        "severity": 2, 
+        "severity_value": 2.9487179487179493, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  Metric id: asw_batch\n  Percentage missing: 29%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Metric 'asw_label' %missing", 
+        "value": 0.34615384615384615, 
+        "severity": 3, 
+        "severity_value": 3.4615384615384612, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  Metric id: asw_label\n  Percentage missing: 35%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Metric 'cell_cycle_conservation' %missing", 
+        "value": 0.32692307692307687, 
+        "severity": 3, 
+        "severity_value": 3.2692307692307687, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  Metric id: cell_cycle_conservation\n  Percentage missing: 33%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Metric 'ari' %missing", 
+        "value": 0.2628205128205128, 
+        "severity": 2, 
+        "severity_value": 2.6282051282051277, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  Metric id: ari\n  Percentage missing: 26%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Metric 'nmi' %missing", 
+        "value": 0.2628205128205128, 
+        "severity": 2, 
+        "severity_value": 2.6282051282051277, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  Metric id: nmi\n  Percentage missing: 26%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Metric 'graph_connectivity' %missing", 
+        "value": 0.25, 
+        "severity": 2, 
+        "severity_value": 2.5, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  Metric id: graph_connectivity\n  Percentage missing: 25%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Metric 'hvg_overlap' %missing", 
+        "value": 0.7628205128205128, 
+        "severity": 3, 
+        "severity_value": 7.628205128205128, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  Metric id: hvg_overlap\n  Percentage missing: 76%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Metric 'isolated_label_asw' %missing", 
+        "value": 0.5192307692307692, 
+        "severity": 3, 
+        "severity_value": 5.192307692307692, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  Metric id: isolated_label_asw\n  Percentage missing: 52%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Metric 'isolated_label_f1' %missing", 
+        "value": 0.39743589743589747, 
+        "severity": 3, 
+        "severity_value": 3.9743589743589745, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  Metric id: isolated_label_f1\n  Percentage missing: 40%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Metric 'kbet' %missing", 
+        "value": 0.46153846153846156, 
+        "severity": 3, 
+        "severity_value": 4.615384615384615, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  Metric id: kbet\n  Percentage missing: 46%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Metric 'ilisi' %missing", 
+        "value": 0.28205128205128205, 
+        "severity": 2, 
+        "severity_value": 2.8205128205128203, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  Metric id: ilisi\n  Percentage missing: 28%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Metric 'clisi' %missing", 
+        "value": 0.28205128205128205, 
+        "severity": 2, 
+        "severity_value": 2.8205128205128203, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  Metric id: clisi\n  Percentage missing: 28%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Metric 'pcr' %missing", 
+        "value": 0.28846153846153844, 
+        "severity": 2, 
+        "severity_value": 2.884615384615384, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  Metric id: pcr\n  Percentage missing: 29%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'embed_cell_types' %missing", 
+        "value": 0.28205128205128205, 
+        "severity": 2, 
+        "severity_value": 2.8205128205128203, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: embed_cell_types\n  Percentage missing: 28%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'embed_cell_types_jittered' %missing", 
+        "value": 0.16666666666666663, 
+        "severity": 1, 
+        "severity_value": 1.6666666666666663, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: embed_cell_types_jittered\n  Percentage missing: 17%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'no_integration' %missing", 
+        "value": 0.15384615384615385, 
+        "severity": 1, 
+        "severity_value": 1.5384615384615385, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: no_integration\n  Percentage missing: 15%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'no_integration_batch' %missing", 
+        "value": 0.1282051282051282, 
+        "severity": 1, 
+        "severity_value": 1.282051282051282, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: no_integration_batch\n  Percentage missing: 13%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'shuffle_integration' %missing", 
+        "value": 0.0641025641025641, 
+        "severity": 0, 
+        "severity_value": 0.641025641025641, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: shuffle_integration\n  Percentage missing: 6%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'shuffle_integration_by_batch' %missing", 
+        "value": 0.14102564102564108, 
+        "severity": 1, 
+        "severity_value": 1.4102564102564108, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: shuffle_integration_by_batch\n  Percentage missing: 14%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'shuffle_integration_by_cell_type' %missing", 
+        "value": 0.0641025641025641, 
+        "severity": 0, 
+        "severity_value": 0.641025641025641, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: shuffle_integration_by_cell_type\n  Percentage missing: 6%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'batchelor_fastmnn' %missing", 
+        "value": 0.14102564102564108, 
+        "severity": 1, 
+        "severity_value": 1.4102564102564108, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: batchelor_fastmnn\n  Percentage missing: 14%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'batchelor_mnn_correct' %missing", 
+        "value": 1.0, 
+        "severity": 3, 
+        "severity_value": 10.0, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: batchelor_mnn_correct\n  Percentage missing: 100%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'bbknn' %missing", 
+        "value": 0.6025641025641025, 
+        "severity": 3, 
+        "severity_value": 6.025641025641025, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: bbknn\n  Percentage missing: 60%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'combat' %missing", 
+        "value": 0.07692307692307687, 
+        "severity": 0, 
+        "severity_value": 0.7692307692307687, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: combat\n  Percentage missing: 8%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'geneformer' %missing", 
+        "value": 0.5897435897435898, 
+        "severity": 3, 
+        "severity_value": 5.897435897435897, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: geneformer\n  Percentage missing: 59%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'harmony' %missing", 
+        "value": 0.17948717948717952, 
+        "severity": 1, 
+        "severity_value": 1.7948717948717952, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: harmony\n  Percentage missing: 18%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'harmonypy' %missing", 
+        "value": 0.16666666666666663, 
+        "severity": 1, 
+        "severity_value": 1.6666666666666663, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: harmonypy\n  Percentage missing: 17%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'liger' %missing", 
+        "value": 0.29487179487179493, 
+        "severity": 2, 
+        "severity_value": 2.9487179487179493, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: liger\n  Percentage missing: 29%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'mnnpy' %missing", 
+        "value": 0.858974358974359, 
+        "severity": 3, 
+        "severity_value": 8.58974358974359, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: mnnpy\n  Percentage missing: 86%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'pyliger' %missing", 
+        "value": 0.42307692307692313, 
+        "severity": 3, 
+        "severity_value": 4.230769230769231, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: pyliger\n  Percentage missing: 42%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'scalex' %missing", 
+        "value": 0.05128205128205121, 
+        "severity": 0, 
+        "severity_value": 0.5128205128205121, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: scalex\n  Percentage missing: 5%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'scanorama' %missing", 
+        "value": 0.038461538461538436, 
+        "severity": 0, 
+        "severity_value": 0.38461538461538436, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: scanorama\n  Percentage missing: 4%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'scanvi' %missing", 
+        "value": 0.15384615384615385, 
+        "severity": 1, 
+        "severity_value": 1.5384615384615385, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: scanvi\n  Percentage missing: 15%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'scgpt_finetuned' %missing", 
+        "value": 1.0, 
+        "severity": 3, 
+        "severity_value": 10.0, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: scgpt_finetuned\n  Percentage missing: 100%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'scgpt_zeroshot' %missing", 
+        "value": 0.4358974358974359, 
+        "severity": 3, 
+        "severity_value": 4.358974358974359, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: scgpt_zeroshot\n  Percentage missing: 44%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'scimilarity' %missing", 
+        "value": 0.4358974358974359, 
+        "severity": 3, 
+        "severity_value": 4.358974358974359, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: scimilarity\n  Percentage missing: 44%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'scprint' %missing", 
+        "value": 1.0, 
+        "severity": 3, 
+        "severity_value": 10.0, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: scprint\n  Percentage missing: 100%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'scvi' %missing", 
+        "value": 0.15384615384615385, 
+        "severity": 1, 
+        "severity_value": 1.5384615384615385, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: scvi\n  Percentage missing: 15%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Method 'uce' %missing", 
+        "value": 0.8717948717948718, 
+        "severity": 3, 
+        "severity_value": 8.717948717948717, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  method id: uce\n  Percentage missing: 87%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Dataset 'cellxgene_census/gtex_v9' %missing", 
+        "value": 0.26627218934911245, 
+        "severity": 2, 
+        "severity_value": 2.6627218934911245, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  dataset id: cellxgene_census/gtex_v9\n  Percentage missing: 27%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Dataset 'cellxgene_census/hypomap' %missing", 
+        "value": 0.4644970414201184, 
+        "severity": 3, 
+        "severity_value": 4.644970414201183, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  dataset id: cellxgene_census/hypomap\n  Percentage missing: 46%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Dataset 'cellxgene_census/dkd' %missing", 
+        "value": 0.36390532544378695, 
+        "severity": 3, 
+        "severity_value": 3.6390532544378695, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  dataset id: cellxgene_census/dkd\n  Percentage missing: 36%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Dataset 'cellxgene_census/immune_cell_atlas' %missing", 
+        "value": 0.2751479289940828, 
+        "severity": 2, 
+        "severity_value": 2.751479289940828, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  dataset id: cellxgene_census/immune_cell_atlas\n  Percentage missing: 28%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Dataset 'cellxgene_census/mouse_pancreas_atlas' %missing", 
+        "value": 0.41420118343195267, 
+        "severity": 3, 
+        "severity_value": 4.1420118343195265, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  dataset id: cellxgene_census/mouse_pancreas_atlas\n  Percentage missing: 41%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Raw results", 
+        "name": "Dataset 'cellxgene_census/tabula_sapiens' %missing", 
+        "value": 0.4023668639053255, 
+        "severity": 3, 
+        "severity_value": 4.023668639053255, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_batch_integration\n  dataset id: cellxgene_census/tabula_sapiens\n  Percentage missing: 40%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types asw_batch", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: asw_batch\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types asw_batch", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: asw_batch\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types_jittered asw_batch", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types_jittered performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: asw_batch\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types_jittered asw_batch", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types_jittered performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: asw_batch\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration asw_batch", 
+        "value": 0.5438, 
+        "severity": 0, 
+        "severity_value": -0.5438, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: asw_batch\n  Worst score: 0.5438%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration asw_batch", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: asw_batch\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration_batch asw_batch", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: asw_batch\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration_batch asw_batch", 
+        "value": 0.4867, 
+        "severity": 0, 
+        "severity_value": 0.24335, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: asw_batch\n  Best score: 0.4867%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration asw_batch", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: asw_batch\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration asw_batch", 
+        "value": 0.9131, 
+        "severity": 0, 
+        "severity_value": 0.45655, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: asw_batch\n  Best score: 0.9131%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_batch asw_batch", 
+        "value": 0.3113, 
+        "severity": 0, 
+        "severity_value": -0.3113, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: asw_batch\n  Worst score: 0.3113%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_batch asw_batch", 
+        "value": 0.7745, 
+        "severity": 0, 
+        "severity_value": 0.38725, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: asw_batch\n  Best score: 0.7745%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_cell_type asw_batch", 
+        "value": 0.335, 
+        "severity": 0, 
+        "severity_value": -0.335, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: asw_batch\n  Worst score: 0.335%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_cell_type asw_batch", 
+        "value": 0.8922, 
+        "severity": 0, 
+        "severity_value": 0.4461, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: asw_batch\n  Best score: 0.8922%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_fastmnn asw_batch", 
+        "value": 0.6044, 
+        "severity": 0, 
+        "severity_value": -0.6044, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_fastmnn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: asw_batch\n  Worst score: 0.6044%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_fastmnn asw_batch", 
+        "value": 0.907, 
+        "severity": 0, 
+        "severity_value": 0.4535, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_fastmnn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: asw_batch\n  Best score: 0.907%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_mnn_correct asw_batch", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_mnn_correct performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: asw_batch\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_mnn_correct asw_batch", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_mnn_correct performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: asw_batch\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score bbknn asw_batch", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method bbknn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: asw_batch\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score bbknn asw_batch", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method bbknn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: asw_batch\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score combat asw_batch", 
+        "value": 0.5658, 
+        "severity": 0, 
+        "severity_value": -0.5658, 
+        "code": "worst_score >= -1", 
+        "message": "Method combat performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: asw_batch\n  Worst score: 0.5658%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score combat asw_batch", 
+        "value": 1.0936, 
+        "severity": 0, 
+        "severity_value": 0.5468, 
+        "code": "best_score <= 2", 
+        "message": "Method combat performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: asw_batch\n  Best score: 1.0936%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score geneformer asw_batch", 
+        "value": -0.9988, 
+        "severity": 0, 
+        "severity_value": 0.9988, 
+        "code": "worst_score >= -1", 
+        "message": "Method geneformer performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: asw_batch\n  Worst score: -0.9988%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score geneformer asw_batch", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method geneformer performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: asw_batch\n  Best score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmony asw_batch", 
+        "value": 0.6462, 
+        "severity": 0, 
+        "severity_value": -0.6462, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmony performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: asw_batch\n  Worst score: 0.6462%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmony asw_batch", 
+        "value": 0.7816, 
+        "severity": 0, 
+        "severity_value": 0.3908, 
+        "code": "best_score <= 2", 
+        "message": "Method harmony performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: asw_batch\n  Best score: 0.7816%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmonypy asw_batch", 
+        "value": 0.6368, 
+        "severity": 0, 
+        "severity_value": -0.6368, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmonypy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: asw_batch\n  Worst score: 0.6368%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmonypy asw_batch", 
+        "value": 0.7929, 
+        "severity": 0, 
+        "severity_value": 0.39645, 
+        "code": "best_score <= 2", 
+        "message": "Method harmonypy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: asw_batch\n  Best score: 0.7929%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score liger asw_batch", 
+        "value": -0.1431, 
+        "severity": 0, 
+        "severity_value": 0.1431, 
+        "code": "worst_score >= -1", 
+        "message": "Method liger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: asw_batch\n  Worst score: -0.1431%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score liger asw_batch", 
+        "value": 0.6612, 
+        "severity": 0, 
+        "severity_value": 0.3306, 
+        "code": "best_score <= 2", 
+        "message": "Method liger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: asw_batch\n  Best score: 0.6612%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score mnnpy asw_batch", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method mnnpy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: asw_batch\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score mnnpy asw_batch", 
+        "value": 0.7025, 
+        "severity": 0, 
+        "severity_value": 0.35125, 
+        "code": "best_score <= 2", 
+        "message": "Method mnnpy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: asw_batch\n  Best score: 0.7025%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score pyliger asw_batch", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method pyliger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: asw_batch\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score pyliger asw_batch", 
+        "value": 0.7145, 
+        "severity": 0, 
+        "severity_value": 0.35725, 
+        "code": "best_score <= 2", 
+        "message": "Method pyliger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: asw_batch\n  Best score: 0.7145%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scalex asw_batch", 
+        "value": 0.4386, 
+        "severity": 0, 
+        "severity_value": -0.4386, 
+        "code": "worst_score >= -1", 
+        "message": "Method scalex performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: asw_batch\n  Worst score: 0.4386%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scalex asw_batch", 
+        "value": 0.6947, 
+        "severity": 0, 
+        "severity_value": 0.34735, 
+        "code": "best_score <= 2", 
+        "message": "Method scalex performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: asw_batch\n  Best score: 0.6947%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanorama asw_batch", 
+        "value": 0.4254, 
+        "severity": 0, 
+        "severity_value": -0.4254, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanorama performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: asw_batch\n  Worst score: 0.4254%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanorama asw_batch", 
+        "value": 0.7837, 
+        "severity": 0, 
+        "severity_value": 0.39185, 
+        "code": "best_score <= 2", 
+        "message": "Method scanorama performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: asw_batch\n  Best score: 0.7837%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanvi asw_batch", 
+        "value": 0.6096, 
+        "severity": 0, 
+        "severity_value": -0.6096, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: asw_batch\n  Worst score: 0.6096%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanvi asw_batch", 
+        "value": 0.7933, 
+        "severity": 0, 
+        "severity_value": 0.39665, 
+        "code": "best_score <= 2", 
+        "message": "Method scanvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: asw_batch\n  Best score: 0.7933%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_finetuned asw_batch", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_finetuned performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: asw_batch\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_finetuned asw_batch", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_finetuned performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: asw_batch\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_zeroshot asw_batch", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_zeroshot performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: asw_batch\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_zeroshot asw_batch", 
+        "value": 0.734, 
+        "severity": 0, 
+        "severity_value": 0.367, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_zeroshot performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: asw_batch\n  Best score: 0.734%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scimilarity asw_batch", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scimilarity performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: asw_batch\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scimilarity asw_batch", 
+        "value": 0.5962, 
+        "severity": 0, 
+        "severity_value": 0.2981, 
+        "code": "best_score <= 2", 
+        "message": "Method scimilarity performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: asw_batch\n  Best score: 0.5962%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scprint asw_batch", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: asw_batch\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scprint asw_batch", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: asw_batch\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scvi asw_batch", 
+        "value": 0.6439, 
+        "severity": 0, 
+        "severity_value": -0.6439, 
+        "code": "worst_score >= -1", 
+        "message": "Method scvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: asw_batch\n  Worst score: 0.6439%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scvi asw_batch", 
+        "value": 0.9829, 
+        "severity": 0, 
+        "severity_value": 0.49145, 
+        "code": "best_score <= 2", 
+        "message": "Method scvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: asw_batch\n  Best score: 0.9829%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score uce asw_batch", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method uce performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: asw_batch\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score uce asw_batch", 
+        "value": 0.8788, 
+        "severity": 0, 
+        "severity_value": 0.4394, 
+        "code": "best_score <= 2", 
+        "message": "Method uce performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: asw_batch\n  Best score: 0.8788%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types asw_label", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: asw_label\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types asw_label", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: asw_label\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types_jittered asw_label", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": -1.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types_jittered performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: asw_label\n  Worst score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types_jittered asw_label", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types_jittered performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: asw_label\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration asw_label", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: asw_label\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration asw_label", 
+        "value": 0.2763, 
+        "severity": 0, 
+        "severity_value": 0.13815, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: asw_label\n  Best score: 0.2763%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration_batch asw_label", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: asw_label\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration_batch asw_label", 
+        "value": 0.22, 
+        "severity": 0, 
+        "severity_value": 0.11, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: asw_label\n  Best score: 0.22%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration asw_label", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: asw_label\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration asw_label", 
+        "value": 0.0897, 
+        "severity": 0, 
+        "severity_value": 0.04485, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: asw_label\n  Best score: 0.0897%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_batch asw_label", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: asw_label\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_batch asw_label", 
+        "value": 0.0442, 
+        "severity": 0, 
+        "severity_value": 0.0221, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: asw_label\n  Best score: 0.0442%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_cell_type asw_label", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: asw_label\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_cell_type asw_label", 
+        "value": 0.2763, 
+        "severity": 0, 
+        "severity_value": 0.13815, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: asw_label\n  Best score: 0.2763%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_fastmnn asw_label", 
+        "value": 0.1231, 
+        "severity": 0, 
+        "severity_value": -0.1231, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_fastmnn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: asw_label\n  Worst score: 0.1231%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_fastmnn asw_label", 
+        "value": 0.3524, 
+        "severity": 0, 
+        "severity_value": 0.1762, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_fastmnn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: asw_label\n  Best score: 0.3524%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_mnn_correct asw_label", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_mnn_correct performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: asw_label\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_mnn_correct asw_label", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_mnn_correct performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: asw_label\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score bbknn asw_label", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method bbknn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: asw_label\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score bbknn asw_label", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method bbknn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: asw_label\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score combat asw_label", 
+        "value": 0.1199, 
+        "severity": 0, 
+        "severity_value": -0.1199, 
+        "code": "worst_score >= -1", 
+        "message": "Method combat performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: asw_label\n  Worst score: 0.1199%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score combat asw_label", 
+        "value": 0.2759, 
+        "severity": 0, 
+        "severity_value": 0.13795, 
+        "code": "best_score <= 2", 
+        "message": "Method combat performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: asw_label\n  Best score: 0.2759%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score geneformer asw_label", 
+        "value": -0.364, 
+        "severity": 0, 
+        "severity_value": 0.364, 
+        "code": "worst_score >= -1", 
+        "message": "Method geneformer performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: asw_label\n  Worst score: -0.364%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score geneformer asw_label", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method geneformer performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: asw_label\n  Best score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmony asw_label", 
+        "value": 0.0876, 
+        "severity": 0, 
+        "severity_value": -0.0876, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmony performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: asw_label\n  Worst score: 0.0876%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmony asw_label", 
+        "value": 0.3441, 
+        "severity": 0, 
+        "severity_value": 0.17205, 
+        "code": "best_score <= 2", 
+        "message": "Method harmony performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: asw_label\n  Best score: 0.3441%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmonypy asw_label", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmonypy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: asw_label\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmonypy asw_label", 
+        "value": 0.336, 
+        "severity": 0, 
+        "severity_value": 0.168, 
+        "code": "best_score <= 2", 
+        "message": "Method harmonypy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: asw_label\n  Best score: 0.336%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score liger asw_label", 
+        "value": -0.0552, 
+        "severity": 0, 
+        "severity_value": 0.0552, 
+        "code": "worst_score >= -1", 
+        "message": "Method liger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: asw_label\n  Worst score: -0.0552%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score liger asw_label", 
+        "value": 0.2875, 
+        "severity": 0, 
+        "severity_value": 0.14375, 
+        "code": "best_score <= 2", 
+        "message": "Method liger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: asw_label\n  Best score: 0.2875%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score mnnpy asw_label", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method mnnpy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: asw_label\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score mnnpy asw_label", 
+        "value": 0.0267, 
+        "severity": 0, 
+        "severity_value": 0.01335, 
+        "code": "best_score <= 2", 
+        "message": "Method mnnpy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: asw_label\n  Best score: 0.0267%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score pyliger asw_label", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method pyliger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: asw_label\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score pyliger asw_label", 
+        "value": 0.2779, 
+        "severity": 0, 
+        "severity_value": 0.13895, 
+        "code": "best_score <= 2", 
+        "message": "Method pyliger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: asw_label\n  Best score: 0.2779%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scalex asw_label", 
+        "value": 0.0535, 
+        "severity": 0, 
+        "severity_value": -0.0535, 
+        "code": "worst_score >= -1", 
+        "message": "Method scalex performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: asw_label\n  Worst score: 0.0535%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scalex asw_label", 
+        "value": 0.3059, 
+        "severity": 0, 
+        "severity_value": 0.15295, 
+        "code": "best_score <= 2", 
+        "message": "Method scalex performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: asw_label\n  Best score: 0.3059%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanorama asw_label", 
+        "value": -0.1098, 
+        "severity": 0, 
+        "severity_value": 0.1098, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanorama performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: asw_label\n  Worst score: -0.1098%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanorama asw_label", 
+        "value": 0.0362, 
+        "severity": 0, 
+        "severity_value": 0.0181, 
+        "code": "best_score <= 2", 
+        "message": "Method scanorama performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: asw_label\n  Best score: 0.0362%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanvi asw_label", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: asw_label\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanvi asw_label", 
+        "value": 0.3485, 
+        "severity": 0, 
+        "severity_value": 0.17425, 
+        "code": "best_score <= 2", 
+        "message": "Method scanvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: asw_label\n  Best score: 0.3485%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_finetuned asw_label", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_finetuned performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: asw_label\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_finetuned asw_label", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_finetuned performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: asw_label\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_zeroshot asw_label", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_zeroshot performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: asw_label\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_zeroshot asw_label", 
+        "value": 0.2848, 
+        "severity": 0, 
+        "severity_value": 0.1424, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_zeroshot performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: asw_label\n  Best score: 0.2848%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scimilarity asw_label", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scimilarity performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: asw_label\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scimilarity asw_label", 
+        "value": 0.4432, 
+        "severity": 0, 
+        "severity_value": 0.2216, 
+        "code": "best_score <= 2", 
+        "message": "Method scimilarity performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: asw_label\n  Best score: 0.4432%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scprint asw_label", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: asw_label\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scprint asw_label", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: asw_label\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scvi asw_label", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: asw_label\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scvi asw_label", 
+        "value": 0.1858, 
+        "severity": 0, 
+        "severity_value": 0.0929, 
+        "code": "best_score <= 2", 
+        "message": "Method scvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: asw_label\n  Best score: 0.1858%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score uce asw_label", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method uce performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: asw_label\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score uce asw_label", 
+        "value": 0.1954, 
+        "severity": 0, 
+        "severity_value": 0.0977, 
+        "code": "best_score <= 2", 
+        "message": "Method uce performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: asw_label\n  Best score: 0.1954%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types cell_cycle_conservation", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: cell_cycle_conservation\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types cell_cycle_conservation", 
+        "value": 0.9365, 
+        "severity": 0, 
+        "severity_value": 0.46825, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: cell_cycle_conservation\n  Best score: 0.9365%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types_jittered cell_cycle_conservation", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types_jittered performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: cell_cycle_conservation\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types_jittered cell_cycle_conservation", 
+        "value": 0.9368, 
+        "severity": 0, 
+        "severity_value": 0.4684, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types_jittered performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: cell_cycle_conservation\n  Best score: 0.9368%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration cell_cycle_conservation", 
+        "value": 0.7664, 
+        "severity": 0, 
+        "severity_value": -0.7664, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: cell_cycle_conservation\n  Worst score: 0.7664%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration cell_cycle_conservation", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: cell_cycle_conservation\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration_batch cell_cycle_conservation", 
+        "value": 0.9425, 
+        "severity": 0, 
+        "severity_value": -0.9425, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: cell_cycle_conservation\n  Worst score: 0.9425%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration_batch cell_cycle_conservation", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: cell_cycle_conservation\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration cell_cycle_conservation", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: cell_cycle_conservation\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration cell_cycle_conservation", 
+        "value": 0.0371, 
+        "severity": 0, 
+        "severity_value": 0.01855, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: cell_cycle_conservation\n  Best score: 0.0371%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_batch cell_cycle_conservation", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: cell_cycle_conservation\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_batch cell_cycle_conservation", 
+        "value": 0.0101, 
+        "severity": 0, 
+        "severity_value": 0.00505, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: cell_cycle_conservation\n  Best score: 0.0101%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_cell_type cell_cycle_conservation", 
+        "value": 0.6034, 
+        "severity": 0, 
+        "severity_value": -0.6034, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: cell_cycle_conservation\n  Worst score: 0.6034%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_cell_type cell_cycle_conservation", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: cell_cycle_conservation\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_fastmnn cell_cycle_conservation", 
+        "value": 0.8939, 
+        "severity": 0, 
+        "severity_value": -0.8939, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_fastmnn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: cell_cycle_conservation\n  Worst score: 0.8939%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_fastmnn cell_cycle_conservation", 
+        "value": 1.0411, 
+        "severity": 0, 
+        "severity_value": 0.52055, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_fastmnn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: cell_cycle_conservation\n  Best score: 1.0411%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_mnn_correct cell_cycle_conservation", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_mnn_correct performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: cell_cycle_conservation\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_mnn_correct cell_cycle_conservation", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_mnn_correct performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: cell_cycle_conservation\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score bbknn cell_cycle_conservation", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method bbknn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: cell_cycle_conservation\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score bbknn cell_cycle_conservation", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method bbknn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: cell_cycle_conservation\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score combat cell_cycle_conservation", 
+        "value": 0.888, 
+        "severity": 0, 
+        "severity_value": -0.888, 
+        "code": "worst_score >= -1", 
+        "message": "Method combat performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: cell_cycle_conservation\n  Worst score: 0.888%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score combat cell_cycle_conservation", 
+        "value": 0.9883, 
+        "severity": 0, 
+        "severity_value": 0.49415, 
+        "code": "best_score <= 2", 
+        "message": "Method combat performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: cell_cycle_conservation\n  Best score: 0.9883%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score geneformer cell_cycle_conservation", 
+        "value": -0.0064, 
+        "severity": 0, 
+        "severity_value": 0.0064, 
+        "code": "worst_score >= -1", 
+        "message": "Method geneformer performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: cell_cycle_conservation\n  Worst score: -0.0064%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score geneformer cell_cycle_conservation", 
+        "value": 0.3171, 
+        "severity": 0, 
+        "severity_value": 0.15855, 
+        "code": "best_score <= 2", 
+        "message": "Method geneformer performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: cell_cycle_conservation\n  Best score: 0.3171%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmony cell_cycle_conservation", 
+        "value": 0.7937, 
+        "severity": 0, 
+        "severity_value": -0.7937, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmony performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: cell_cycle_conservation\n  Worst score: 0.7937%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmony cell_cycle_conservation", 
+        "value": 0.9816, 
+        "severity": 0, 
+        "severity_value": 0.4908, 
+        "code": "best_score <= 2", 
+        "message": "Method harmony performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: cell_cycle_conservation\n  Best score: 0.9816%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmonypy cell_cycle_conservation", 
+        "value": 0.7925, 
+        "severity": 0, 
+        "severity_value": -0.7925, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmonypy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: cell_cycle_conservation\n  Worst score: 0.7925%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmonypy cell_cycle_conservation", 
+        "value": 0.9821, 
+        "severity": 0, 
+        "severity_value": 0.49105, 
+        "code": "best_score <= 2", 
+        "message": "Method harmonypy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: cell_cycle_conservation\n  Best score: 0.9821%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score liger cell_cycle_conservation", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method liger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: cell_cycle_conservation\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score liger cell_cycle_conservation", 
+        "value": 0.7877, 
+        "severity": 0, 
+        "severity_value": 0.39385, 
+        "code": "best_score <= 2", 
+        "message": "Method liger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: cell_cycle_conservation\n  Best score: 0.7877%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score mnnpy cell_cycle_conservation", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method mnnpy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: cell_cycle_conservation\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score mnnpy cell_cycle_conservation", 
+        "value": 0.4007, 
+        "severity": 0, 
+        "severity_value": 0.20035, 
+        "code": "best_score <= 2", 
+        "message": "Method mnnpy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: cell_cycle_conservation\n  Best score: 0.4007%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score pyliger cell_cycle_conservation", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method pyliger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: cell_cycle_conservation\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score pyliger cell_cycle_conservation", 
+        "value": 1.0434, 
+        "severity": 0, 
+        "severity_value": 0.5217, 
+        "code": "best_score <= 2", 
+        "message": "Method pyliger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: cell_cycle_conservation\n  Best score: 1.0434%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scalex cell_cycle_conservation", 
+        "value": 0.387, 
+        "severity": 0, 
+        "severity_value": -0.387, 
+        "code": "worst_score >= -1", 
+        "message": "Method scalex performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: cell_cycle_conservation\n  Worst score: 0.387%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scalex cell_cycle_conservation", 
+        "value": 0.8781, 
+        "severity": 0, 
+        "severity_value": 0.43905, 
+        "code": "best_score <= 2", 
+        "message": "Method scalex performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: cell_cycle_conservation\n  Best score: 0.8781%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanorama cell_cycle_conservation", 
+        "value": -0.0016, 
+        "severity": 0, 
+        "severity_value": 0.0016, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanorama performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: cell_cycle_conservation\n  Worst score: -0.0016%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanorama cell_cycle_conservation", 
+        "value": 0.4093, 
+        "severity": 0, 
+        "severity_value": 0.20465, 
+        "code": "best_score <= 2", 
+        "message": "Method scanorama performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: cell_cycle_conservation\n  Best score: 0.4093%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanvi cell_cycle_conservation", 
+        "value": 0.7614, 
+        "severity": 0, 
+        "severity_value": -0.7614, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: cell_cycle_conservation\n  Worst score: 0.7614%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanvi cell_cycle_conservation", 
+        "value": 1.1501, 
+        "severity": 0, 
+        "severity_value": 0.57505, 
+        "code": "best_score <= 2", 
+        "message": "Method scanvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: cell_cycle_conservation\n  Best score: 1.1501%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_finetuned cell_cycle_conservation", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_finetuned performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: cell_cycle_conservation\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_finetuned cell_cycle_conservation", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_finetuned performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: cell_cycle_conservation\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_zeroshot cell_cycle_conservation", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_zeroshot performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: cell_cycle_conservation\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_zeroshot cell_cycle_conservation", 
+        "value": 1.0493, 
+        "severity": 0, 
+        "severity_value": 0.52465, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_zeroshot performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: cell_cycle_conservation\n  Best score: 1.0493%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scimilarity cell_cycle_conservation", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scimilarity performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: cell_cycle_conservation\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scimilarity cell_cycle_conservation", 
+        "value": 0.7917, 
+        "severity": 0, 
+        "severity_value": 0.39585, 
+        "code": "best_score <= 2", 
+        "message": "Method scimilarity performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: cell_cycle_conservation\n  Best score: 0.7917%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scprint cell_cycle_conservation", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: cell_cycle_conservation\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scprint cell_cycle_conservation", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: cell_cycle_conservation\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scvi cell_cycle_conservation", 
+        "value": 0.5226, 
+        "severity": 0, 
+        "severity_value": -0.5226, 
+        "code": "worst_score >= -1", 
+        "message": "Method scvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: cell_cycle_conservation\n  Worst score: 0.5226%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scvi cell_cycle_conservation", 
+        "value": 1.0868, 
+        "severity": 0, 
+        "severity_value": 0.5434, 
+        "code": "best_score <= 2", 
+        "message": "Method scvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: cell_cycle_conservation\n  Best score: 1.0868%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score uce cell_cycle_conservation", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method uce performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: cell_cycle_conservation\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score uce cell_cycle_conservation", 
+        "value": 0.9803, 
+        "severity": 0, 
+        "severity_value": 0.49015, 
+        "code": "best_score <= 2", 
+        "message": "Method uce performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: cell_cycle_conservation\n  Best score: 0.9803%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types ari", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": -1.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: ari\n  Worst score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types ari", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: ari\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types_jittered ari", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": -1.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types_jittered performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: ari\n  Worst score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types_jittered ari", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types_jittered performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: ari\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration ari", 
+        "value": 0.2567, 
+        "severity": 0, 
+        "severity_value": -0.2567, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: ari\n  Worst score: 0.2567%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration ari", 
+        "value": 0.6958, 
+        "severity": 0, 
+        "severity_value": 0.3479, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: ari\n  Best score: 0.6958%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration_batch ari", 
+        "value": 0.106, 
+        "severity": 0, 
+        "severity_value": -0.106, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: ari\n  Worst score: 0.106%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration_batch ari", 
+        "value": 0.2885, 
+        "severity": 0, 
+        "severity_value": 0.14425, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: ari\n  Best score: 0.2885%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration ari", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: ari\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration ari", 
+        "value": 0.0002, 
+        "severity": 0, 
+        "severity_value": 0.0001, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: ari\n  Best score: 0.0002%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_batch ari", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: ari\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_batch ari", 
+        "value": 0.0806, 
+        "severity": 0, 
+        "severity_value": 0.0403, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: ari\n  Best score: 0.0806%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_cell_type ari", 
+        "value": 0.2857, 
+        "severity": 0, 
+        "severity_value": -0.2857, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: ari\n  Worst score: 0.2857%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_cell_type ari", 
+        "value": 0.719, 
+        "severity": 0, 
+        "severity_value": 0.3595, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: ari\n  Best score: 0.719%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_fastmnn ari", 
+        "value": 0.3537, 
+        "severity": 0, 
+        "severity_value": -0.3537, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_fastmnn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: ari\n  Worst score: 0.3537%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_fastmnn ari", 
+        "value": 0.7601, 
+        "severity": 0, 
+        "severity_value": 0.38005, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_fastmnn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: ari\n  Best score: 0.7601%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_mnn_correct ari", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_mnn_correct performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: ari\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_mnn_correct ari", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_mnn_correct performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: ari\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score bbknn ari", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method bbknn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: ari\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score bbknn ari", 
+        "value": 0.7951, 
+        "severity": 0, 
+        "severity_value": 0.39755, 
+        "code": "best_score <= 2", 
+        "message": "Method bbknn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: ari\n  Best score: 0.7951%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score combat ari", 
+        "value": 0.2244, 
+        "severity": 0, 
+        "severity_value": -0.2244, 
+        "code": "worst_score >= -1", 
+        "message": "Method combat performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: ari\n  Worst score: 0.2244%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score combat ari", 
+        "value": 0.7674, 
+        "severity": 0, 
+        "severity_value": 0.3837, 
+        "code": "best_score <= 2", 
+        "message": "Method combat performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: ari\n  Best score: 0.7674%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score geneformer ari", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method geneformer performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: ari\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score geneformer ari", 
+        "value": 0.0562, 
+        "severity": 0, 
+        "severity_value": 0.0281, 
+        "code": "best_score <= 2", 
+        "message": "Method geneformer performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: ari\n  Best score: 0.0562%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmony ari", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmony performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: ari\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmony ari", 
+        "value": 0.8171, 
+        "severity": 0, 
+        "severity_value": 0.40855, 
+        "code": "best_score <= 2", 
+        "message": "Method harmony performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: ari\n  Best score: 0.8171%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmonypy ari", 
+        "value": 0.4134, 
+        "severity": 0, 
+        "severity_value": -0.4134, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmonypy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: ari\n  Worst score: 0.4134%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmonypy ari", 
+        "value": 0.8205, 
+        "severity": 0, 
+        "severity_value": 0.41025, 
+        "code": "best_score <= 2", 
+        "message": "Method harmonypy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: ari\n  Best score: 0.8205%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score liger ari", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method liger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: ari\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score liger ari", 
+        "value": 0.7464, 
+        "severity": 0, 
+        "severity_value": 0.3732, 
+        "code": "best_score <= 2", 
+        "message": "Method liger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: ari\n  Best score: 0.7464%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score mnnpy ari", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method mnnpy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: ari\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score mnnpy ari", 
+        "value": 0.1669, 
+        "severity": 0, 
+        "severity_value": 0.08345, 
+        "code": "best_score <= 2", 
+        "message": "Method mnnpy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: ari\n  Best score: 0.1669%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score pyliger ari", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method pyliger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: ari\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score pyliger ari", 
+        "value": 0.6634, 
+        "severity": 0, 
+        "severity_value": 0.3317, 
+        "code": "best_score <= 2", 
+        "message": "Method pyliger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: ari\n  Best score: 0.6634%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scalex ari", 
+        "value": 0.3469, 
+        "severity": 0, 
+        "severity_value": -0.3469, 
+        "code": "worst_score >= -1", 
+        "message": "Method scalex performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: ari\n  Worst score: 0.3469%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scalex ari", 
+        "value": 0.586, 
+        "severity": 0, 
+        "severity_value": 0.293, 
+        "code": "best_score <= 2", 
+        "message": "Method scalex performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: ari\n  Best score: 0.586%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanorama ari", 
+        "value": -0.0001, 
+        "severity": 0, 
+        "severity_value": 0.0001, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanorama performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: ari\n  Worst score: -0.0001%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanorama ari", 
+        "value": 0.1729, 
+        "severity": 0, 
+        "severity_value": 0.08645, 
+        "code": "best_score <= 2", 
+        "message": "Method scanorama performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: ari\n  Best score: 0.1729%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanvi ari", 
+        "value": 0.2176, 
+        "severity": 0, 
+        "severity_value": -0.2176, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: ari\n  Worst score: 0.2176%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanvi ari", 
+        "value": 0.8839, 
+        "severity": 0, 
+        "severity_value": 0.44195, 
+        "code": "best_score <= 2", 
+        "message": "Method scanvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: ari\n  Best score: 0.8839%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_finetuned ari", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_finetuned performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: ari\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_finetuned ari", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_finetuned performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: ari\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_zeroshot ari", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_zeroshot performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: ari\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_zeroshot ari", 
+        "value": 0.771, 
+        "severity": 0, 
+        "severity_value": 0.3855, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_zeroshot performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: ari\n  Best score: 0.771%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scimilarity ari", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scimilarity performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: ari\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scimilarity ari", 
+        "value": 0.7104, 
+        "severity": 0, 
+        "severity_value": 0.3552, 
+        "code": "best_score <= 2", 
+        "message": "Method scimilarity performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: ari\n  Best score: 0.7104%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scprint ari", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: ari\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scprint ari", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: ari\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scvi ari", 
+        "value": 0.2359, 
+        "severity": 0, 
+        "severity_value": -0.2359, 
+        "code": "worst_score >= -1", 
+        "message": "Method scvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: ari\n  Worst score: 0.2359%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scvi ari", 
+        "value": 0.8517, 
+        "severity": 0, 
+        "severity_value": 0.42585, 
+        "code": "best_score <= 2", 
+        "message": "Method scvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: ari\n  Best score: 0.8517%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score uce ari", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method uce performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: ari\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score uce ari", 
+        "value": 0.5011, 
+        "severity": 0, 
+        "severity_value": 0.25055, 
+        "code": "best_score <= 2", 
+        "message": "Method uce performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: ari\n  Best score: 0.5011%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types nmi", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": -1.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: nmi\n  Worst score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types nmi", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: nmi\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types_jittered nmi", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": -1.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types_jittered performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: nmi\n  Worst score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types_jittered nmi", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types_jittered performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: nmi\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration nmi", 
+        "value": 0.6017, 
+        "severity": 0, 
+        "severity_value": -0.6017, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: nmi\n  Worst score: 0.6017%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration nmi", 
+        "value": 0.8291, 
+        "severity": 0, 
+        "severity_value": 0.41455, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: nmi\n  Best score: 0.8291%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration_batch nmi", 
+        "value": 0.3886, 
+        "severity": 0, 
+        "severity_value": -0.3886, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: nmi\n  Worst score: 0.3886%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration_batch nmi", 
+        "value": 0.6862, 
+        "severity": 0, 
+        "severity_value": 0.3431, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: nmi\n  Best score: 0.6862%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration nmi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: nmi\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration nmi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: nmi\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_batch nmi", 
+        "value": 0.003, 
+        "severity": 0, 
+        "severity_value": -0.003, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: nmi\n  Worst score: 0.003%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_batch nmi", 
+        "value": 0.2072, 
+        "severity": 0, 
+        "severity_value": 0.1036, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: nmi\n  Best score: 0.2072%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_cell_type nmi", 
+        "value": 0.6148, 
+        "severity": 0, 
+        "severity_value": -0.6148, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: nmi\n  Worst score: 0.6148%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_cell_type nmi", 
+        "value": 0.837, 
+        "severity": 0, 
+        "severity_value": 0.4185, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: nmi\n  Best score: 0.837%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_fastmnn nmi", 
+        "value": 0.663, 
+        "severity": 0, 
+        "severity_value": -0.663, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_fastmnn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: nmi\n  Worst score: 0.663%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_fastmnn nmi", 
+        "value": 0.8271, 
+        "severity": 0, 
+        "severity_value": 0.41355, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_fastmnn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: nmi\n  Best score: 0.8271%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_mnn_correct nmi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_mnn_correct performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: nmi\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_mnn_correct nmi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_mnn_correct performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: nmi\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score bbknn nmi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method bbknn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: nmi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score bbknn nmi", 
+        "value": 0.8151, 
+        "severity": 0, 
+        "severity_value": 0.40755, 
+        "code": "best_score <= 2", 
+        "message": "Method bbknn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: nmi\n  Best score: 0.8151%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score combat nmi", 
+        "value": 0.5943, 
+        "severity": 0, 
+        "severity_value": -0.5943, 
+        "code": "worst_score >= -1", 
+        "message": "Method combat performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: nmi\n  Worst score: 0.5943%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score combat nmi", 
+        "value": 0.8303, 
+        "severity": 0, 
+        "severity_value": 0.41515, 
+        "code": "best_score <= 2", 
+        "message": "Method combat performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: nmi\n  Best score: 0.8303%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score geneformer nmi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method geneformer performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: nmi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score geneformer nmi", 
+        "value": 0.3942, 
+        "severity": 0, 
+        "severity_value": 0.1971, 
+        "code": "best_score <= 2", 
+        "message": "Method geneformer performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: nmi\n  Best score: 0.3942%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmony nmi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmony performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: nmi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmony nmi", 
+        "value": 0.8293, 
+        "severity": 0, 
+        "severity_value": 0.41465, 
+        "code": "best_score <= 2", 
+        "message": "Method harmony performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: nmi\n  Best score: 0.8293%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmonypy nmi", 
+        "value": 0.6602, 
+        "severity": 0, 
+        "severity_value": -0.6602, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmonypy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: nmi\n  Worst score: 0.6602%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmonypy nmi", 
+        "value": 0.823, 
+        "severity": 0, 
+        "severity_value": 0.4115, 
+        "code": "best_score <= 2", 
+        "message": "Method harmonypy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: nmi\n  Best score: 0.823%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score liger nmi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method liger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: nmi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score liger nmi", 
+        "value": 0.7999, 
+        "severity": 0, 
+        "severity_value": 0.39995, 
+        "code": "best_score <= 2", 
+        "message": "Method liger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: nmi\n  Best score: 0.7999%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score mnnpy nmi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method mnnpy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: nmi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score mnnpy nmi", 
+        "value": 0.2281, 
+        "severity": 0, 
+        "severity_value": 0.11405, 
+        "code": "best_score <= 2", 
+        "message": "Method mnnpy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: nmi\n  Best score: 0.2281%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score pyliger nmi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method pyliger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: nmi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score pyliger nmi", 
+        "value": 0.7615, 
+        "severity": 0, 
+        "severity_value": 0.38075, 
+        "code": "best_score <= 2", 
+        "message": "Method pyliger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: nmi\n  Best score: 0.7615%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scalex nmi", 
+        "value": 0.568, 
+        "severity": 0, 
+        "severity_value": -0.568, 
+        "code": "worst_score >= -1", 
+        "message": "Method scalex performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: nmi\n  Worst score: 0.568%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scalex nmi", 
+        "value": 0.7219, 
+        "severity": 0, 
+        "severity_value": 0.36095, 
+        "code": "best_score <= 2", 
+        "message": "Method scalex performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: nmi\n  Best score: 0.7219%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanorama nmi", 
+        "value": -6.2406e-06, 
+        "severity": 0, 
+        "severity_value": 6.2406e-06, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanorama performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: nmi\n  Worst score: -6.2406e-06%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanorama nmi", 
+        "value": 0.2249, 
+        "severity": 0, 
+        "severity_value": 0.11245, 
+        "code": "best_score <= 2", 
+        "message": "Method scanorama performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: nmi\n  Best score: 0.2249%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanvi nmi", 
+        "value": 0.5887, 
+        "severity": 0, 
+        "severity_value": -0.5887, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: nmi\n  Worst score: 0.5887%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanvi nmi", 
+        "value": 0.8929, 
+        "severity": 0, 
+        "severity_value": 0.44645, 
+        "code": "best_score <= 2", 
+        "message": "Method scanvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: nmi\n  Best score: 0.8929%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_finetuned nmi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_finetuned performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: nmi\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_finetuned nmi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_finetuned performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: nmi\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_zeroshot nmi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_zeroshot performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: nmi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_zeroshot nmi", 
+        "value": 0.7923, 
+        "severity": 0, 
+        "severity_value": 0.39615, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_zeroshot performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: nmi\n  Best score: 0.7923%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scimilarity nmi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scimilarity performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: nmi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scimilarity nmi", 
+        "value": 0.7974, 
+        "severity": 0, 
+        "severity_value": 0.3987, 
+        "code": "best_score <= 2", 
+        "message": "Method scimilarity performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: nmi\n  Best score: 0.7974%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scprint nmi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: nmi\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scprint nmi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: nmi\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scvi nmi", 
+        "value": 0.5919, 
+        "severity": 0, 
+        "severity_value": -0.5919, 
+        "code": "worst_score >= -1", 
+        "message": "Method scvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: nmi\n  Worst score: 0.5919%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scvi nmi", 
+        "value": 0.8418, 
+        "severity": 0, 
+        "severity_value": 0.4209, 
+        "code": "best_score <= 2", 
+        "message": "Method scvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: nmi\n  Best score: 0.8418%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score uce nmi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method uce performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: nmi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score uce nmi", 
+        "value": 0.716, 
+        "severity": 0, 
+        "severity_value": 0.358, 
+        "code": "best_score <= 2", 
+        "message": "Method uce performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: nmi\n  Best score: 0.716%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types graph_connectivity", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: graph_connectivity\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types graph_connectivity", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: graph_connectivity\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types_jittered graph_connectivity", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": -1.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types_jittered performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: graph_connectivity\n  Worst score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types_jittered graph_connectivity", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types_jittered performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: graph_connectivity\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration graph_connectivity", 
+        "value": 0.8764, 
+        "severity": 0, 
+        "severity_value": -0.8764, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: graph_connectivity\n  Worst score: 0.8764%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration graph_connectivity", 
+        "value": 0.9758, 
+        "severity": 0, 
+        "severity_value": 0.4879, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: graph_connectivity\n  Best score: 0.9758%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration_batch graph_connectivity", 
+        "value": 0.368, 
+        "severity": 0, 
+        "severity_value": -0.368, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: graph_connectivity\n  Worst score: 0.368%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration_batch graph_connectivity", 
+        "value": 0.8315, 
+        "severity": 0, 
+        "severity_value": 0.41575, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: graph_connectivity\n  Best score: 0.8315%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration graph_connectivity", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: graph_connectivity\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration graph_connectivity", 
+        "value": 0.0037, 
+        "severity": 0, 
+        "severity_value": 0.00185, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: graph_connectivity\n  Best score: 0.0037%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_batch graph_connectivity", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: graph_connectivity\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_batch graph_connectivity", 
+        "value": 0.2438, 
+        "severity": 0, 
+        "severity_value": 0.1219, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: graph_connectivity\n  Best score: 0.2438%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_cell_type graph_connectivity", 
+        "value": 0.8769, 
+        "severity": 0, 
+        "severity_value": -0.8769, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: graph_connectivity\n  Worst score: 0.8769%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_cell_type graph_connectivity", 
+        "value": 0.9751, 
+        "severity": 0, 
+        "severity_value": 0.48755, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: graph_connectivity\n  Best score: 0.9751%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_fastmnn graph_connectivity", 
+        "value": 0.8077, 
+        "severity": 0, 
+        "severity_value": -0.8077, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_fastmnn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: graph_connectivity\n  Worst score: 0.8077%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_fastmnn graph_connectivity", 
+        "value": 0.965, 
+        "severity": 0, 
+        "severity_value": 0.4825, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_fastmnn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: graph_connectivity\n  Best score: 0.965%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_mnn_correct graph_connectivity", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_mnn_correct performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: graph_connectivity\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_mnn_correct graph_connectivity", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_mnn_correct performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: graph_connectivity\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score bbknn graph_connectivity", 
+        "value": 0.8422, 
+        "severity": 0, 
+        "severity_value": -0.8422, 
+        "code": "worst_score >= -1", 
+        "message": "Method bbknn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: graph_connectivity\n  Worst score: 0.8422%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score bbknn graph_connectivity", 
+        "value": 0.9976, 
+        "severity": 0, 
+        "severity_value": 0.4988, 
+        "code": "best_score <= 2", 
+        "message": "Method bbknn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: graph_connectivity\n  Best score: 0.9976%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score combat graph_connectivity", 
+        "value": 0.8753, 
+        "severity": 0, 
+        "severity_value": -0.8753, 
+        "code": "worst_score >= -1", 
+        "message": "Method combat performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: graph_connectivity\n  Worst score: 0.8753%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score combat graph_connectivity", 
+        "value": 0.9667, 
+        "severity": 0, 
+        "severity_value": 0.48335, 
+        "code": "best_score <= 2", 
+        "message": "Method combat performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: graph_connectivity\n  Best score: 0.9667%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score geneformer graph_connectivity", 
+        "value": -0.3102, 
+        "severity": 0, 
+        "severity_value": 0.3102, 
+        "code": "worst_score >= -1", 
+        "message": "Method geneformer performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: graph_connectivity\n  Worst score: -0.3102%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score geneformer graph_connectivity", 
+        "value": 0.0253, 
+        "severity": 0, 
+        "severity_value": 0.01265, 
+        "code": "best_score <= 2", 
+        "message": "Method geneformer performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: graph_connectivity\n  Best score: 0.0253%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmony graph_connectivity", 
+        "value": 0.8393, 
+        "severity": 0, 
+        "severity_value": -0.8393, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmony performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: graph_connectivity\n  Worst score: 0.8393%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmony graph_connectivity", 
+        "value": 0.9686, 
+        "severity": 0, 
+        "severity_value": 0.4843, 
+        "code": "best_score <= 2", 
+        "message": "Method harmony performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: graph_connectivity\n  Best score: 0.9686%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmonypy graph_connectivity", 
+        "value": 0.8285, 
+        "severity": 0, 
+        "severity_value": -0.8285, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmonypy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: graph_connectivity\n  Worst score: 0.8285%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmonypy graph_connectivity", 
+        "value": 0.9689, 
+        "severity": 0, 
+        "severity_value": 0.48445, 
+        "code": "best_score <= 2", 
+        "message": "Method harmonypy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: graph_connectivity\n  Best score: 0.9689%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score liger graph_connectivity", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method liger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: graph_connectivity\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score liger graph_connectivity", 
+        "value": 0.9508, 
+        "severity": 0, 
+        "severity_value": 0.4754, 
+        "code": "best_score <= 2", 
+        "message": "Method liger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: graph_connectivity\n  Best score: 0.9508%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score mnnpy graph_connectivity", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method mnnpy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: graph_connectivity\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score mnnpy graph_connectivity", 
+        "value": 0.3974, 
+        "severity": 0, 
+        "severity_value": 0.1987, 
+        "code": "best_score <= 2", 
+        "message": "Method mnnpy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: graph_connectivity\n  Best score: 0.3974%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score pyliger graph_connectivity", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method pyliger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: graph_connectivity\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score pyliger graph_connectivity", 
+        "value": 0.9576, 
+        "severity": 0, 
+        "severity_value": 0.4788, 
+        "code": "best_score <= 2", 
+        "message": "Method pyliger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: graph_connectivity\n  Best score: 0.9576%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scalex graph_connectivity", 
+        "value": 0.7199, 
+        "severity": 0, 
+        "severity_value": -0.7199, 
+        "code": "worst_score >= -1", 
+        "message": "Method scalex performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: graph_connectivity\n  Worst score: 0.7199%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scalex graph_connectivity", 
+        "value": 0.9039, 
+        "severity": 0, 
+        "severity_value": 0.45195, 
+        "code": "best_score <= 2", 
+        "message": "Method scalex performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: graph_connectivity\n  Best score: 0.9039%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanorama graph_connectivity", 
+        "value": 0.0045, 
+        "severity": 0, 
+        "severity_value": -0.0045, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanorama performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: graph_connectivity\n  Worst score: 0.0045%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanorama graph_connectivity", 
+        "value": 0.3972, 
+        "severity": 0, 
+        "severity_value": 0.1986, 
+        "code": "best_score <= 2", 
+        "message": "Method scanorama performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: graph_connectivity\n  Best score: 0.3972%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanvi graph_connectivity", 
+        "value": 0.8694, 
+        "severity": 0, 
+        "severity_value": -0.8694, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: graph_connectivity\n  Worst score: 0.8694%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanvi graph_connectivity", 
+        "value": 0.9976, 
+        "severity": 0, 
+        "severity_value": 0.4988, 
+        "code": "best_score <= 2", 
+        "message": "Method scanvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: graph_connectivity\n  Best score: 0.9976%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_finetuned graph_connectivity", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_finetuned performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: graph_connectivity\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_finetuned graph_connectivity", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_finetuned performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: graph_connectivity\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_zeroshot graph_connectivity", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_zeroshot performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: graph_connectivity\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_zeroshot graph_connectivity", 
+        "value": 0.969, 
+        "severity": 0, 
+        "severity_value": 0.4845, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_zeroshot performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: graph_connectivity\n  Best score: 0.969%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scimilarity graph_connectivity", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scimilarity performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: graph_connectivity\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scimilarity graph_connectivity", 
+        "value": 0.9703, 
+        "severity": 0, 
+        "severity_value": 0.48515, 
+        "code": "best_score <= 2", 
+        "message": "Method scimilarity performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: graph_connectivity\n  Best score: 0.9703%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scprint graph_connectivity", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: graph_connectivity\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scprint graph_connectivity", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: graph_connectivity\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scvi graph_connectivity", 
+        "value": 0.8813, 
+        "severity": 0, 
+        "severity_value": -0.8813, 
+        "code": "worst_score >= -1", 
+        "message": "Method scvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: graph_connectivity\n  Worst score: 0.8813%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scvi graph_connectivity", 
+        "value": 0.9832, 
+        "severity": 0, 
+        "severity_value": 0.4916, 
+        "code": "best_score <= 2", 
+        "message": "Method scvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: graph_connectivity\n  Best score: 0.9832%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score uce graph_connectivity", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method uce performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: graph_connectivity\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score uce graph_connectivity", 
+        "value": 0.9408, 
+        "severity": 0, 
+        "severity_value": 0.4704, 
+        "code": "best_score <= 2", 
+        "message": "Method uce performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: graph_connectivity\n  Best score: 0.9408%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types_jittered hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types_jittered performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types_jittered hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types_jittered performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration_batch hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration_batch hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_batch hvg_overlap", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": -1.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: hvg_overlap\n  Worst score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_batch hvg_overlap", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: hvg_overlap\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_cell_type hvg_overlap", 
+        "value": 0.0691, 
+        "severity": 0, 
+        "severity_value": -0.0691, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: hvg_overlap\n  Worst score: 0.0691%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_cell_type hvg_overlap", 
+        "value": 0.2642, 
+        "severity": 0, 
+        "severity_value": 0.1321, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: hvg_overlap\n  Best score: 0.2642%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_fastmnn hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_fastmnn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_fastmnn hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_fastmnn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_mnn_correct hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_mnn_correct performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_mnn_correct hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_mnn_correct performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score bbknn hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method bbknn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score bbknn hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method bbknn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score combat hvg_overlap", 
+        "value": 0.0512, 
+        "severity": 0, 
+        "severity_value": -0.0512, 
+        "code": "worst_score >= -1", 
+        "message": "Method combat performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: hvg_overlap\n  Worst score: 0.0512%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score combat hvg_overlap", 
+        "value": 0.1201, 
+        "severity": 0, 
+        "severity_value": 0.06005, 
+        "code": "best_score <= 2", 
+        "message": "Method combat performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: hvg_overlap\n  Best score: 0.1201%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score geneformer hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method geneformer performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score geneformer hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method geneformer performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmony hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmony performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmony hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method harmony performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmonypy hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmonypy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmonypy hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method harmonypy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score liger hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method liger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score liger hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method liger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score mnnpy hvg_overlap", 
+        "value": -0.6721, 
+        "severity": 0, 
+        "severity_value": 0.6721, 
+        "code": "worst_score >= -1", 
+        "message": "Method mnnpy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: hvg_overlap\n  Worst score: -0.6721%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score mnnpy hvg_overlap", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method mnnpy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: hvg_overlap\n  Best score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score pyliger hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method pyliger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score pyliger hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method pyliger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scalex hvg_overlap", 
+        "value": -1.2485, 
+        "severity": 1, 
+        "severity_value": 1.2485, 
+        "code": "worst_score >= -1", 
+        "message": "Method scalex performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: hvg_overlap\n  Worst score: -1.2485%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scalex hvg_overlap", 
+        "value": -0.4188, 
+        "severity": 0, 
+        "severity_value": -0.2094, 
+        "code": "best_score <= 2", 
+        "message": "Method scalex performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: hvg_overlap\n  Best score: -0.4188%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanorama hvg_overlap", 
+        "value": -1.5312, 
+        "severity": 1, 
+        "severity_value": 1.5312, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanorama performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: hvg_overlap\n  Worst score: -1.5312%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanorama hvg_overlap", 
+        "value": -0.4673, 
+        "severity": 0, 
+        "severity_value": -0.23365, 
+        "code": "best_score <= 2", 
+        "message": "Method scanorama performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: hvg_overlap\n  Best score: -0.4673%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanvi hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanvi hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scanvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_finetuned hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_finetuned performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_finetuned hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_finetuned performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_zeroshot hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_zeroshot performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_zeroshot hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_zeroshot performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scimilarity hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scimilarity performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scimilarity hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scimilarity performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scprint hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scprint hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scvi hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scvi hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score uce hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method uce performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: hvg_overlap\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score uce hvg_overlap", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method uce performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: hvg_overlap\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types isolated_label_asw", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: isolated_label_asw\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types isolated_label_asw", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: isolated_label_asw\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types_jittered isolated_label_asw", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types_jittered performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: isolated_label_asw\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types_jittered isolated_label_asw", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types_jittered performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: isolated_label_asw\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration isolated_label_asw", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: isolated_label_asw\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration isolated_label_asw", 
+        "value": 0.6433, 
+        "severity": 0, 
+        "severity_value": 0.32165, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: isolated_label_asw\n  Best score: 0.6433%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration_batch isolated_label_asw", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: isolated_label_asw\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration_batch isolated_label_asw", 
+        "value": 0.1807, 
+        "severity": 0, 
+        "severity_value": 0.09035, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: isolated_label_asw\n  Best score: 0.1807%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration isolated_label_asw", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: isolated_label_asw\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration isolated_label_asw", 
+        "value": 0.2565, 
+        "severity": 0, 
+        "severity_value": 0.12825, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: isolated_label_asw\n  Best score: 0.2565%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_batch isolated_label_asw", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: isolated_label_asw\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_batch isolated_label_asw", 
+        "value": 0.0499, 
+        "severity": 0, 
+        "severity_value": 0.02495, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: isolated_label_asw\n  Best score: 0.0499%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_cell_type isolated_label_asw", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: isolated_label_asw\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_cell_type isolated_label_asw", 
+        "value": 0.6433, 
+        "severity": 0, 
+        "severity_value": 0.32165, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: isolated_label_asw\n  Best score: 0.6433%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_fastmnn isolated_label_asw", 
+        "value": -0.1001, 
+        "severity": 0, 
+        "severity_value": 0.1001, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_fastmnn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: isolated_label_asw\n  Worst score: -0.1001%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_fastmnn isolated_label_asw", 
+        "value": 0.638, 
+        "severity": 0, 
+        "severity_value": 0.319, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_fastmnn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: isolated_label_asw\n  Best score: 0.638%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_mnn_correct isolated_label_asw", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_mnn_correct performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: isolated_label_asw\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_mnn_correct isolated_label_asw", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_mnn_correct performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: isolated_label_asw\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score bbknn isolated_label_asw", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method bbknn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: isolated_label_asw\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score bbknn isolated_label_asw", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method bbknn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: isolated_label_asw\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score combat isolated_label_asw", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method combat performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: isolated_label_asw\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score combat isolated_label_asw", 
+        "value": 0.4623, 
+        "severity": 0, 
+        "severity_value": 0.23115, 
+        "code": "best_score <= 2", 
+        "message": "Method combat performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: isolated_label_asw\n  Best score: 0.4623%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score geneformer isolated_label_asw", 
+        "value": -0.3664, 
+        "severity": 0, 
+        "severity_value": 0.3664, 
+        "code": "worst_score >= -1", 
+        "message": "Method geneformer performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: isolated_label_asw\n  Worst score: -0.3664%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score geneformer isolated_label_asw", 
+        "value": 0.0194, 
+        "severity": 0, 
+        "severity_value": 0.0097, 
+        "code": "best_score <= 2", 
+        "message": "Method geneformer performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: isolated_label_asw\n  Best score: 0.0194%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmony isolated_label_asw", 
+        "value": -0.0584, 
+        "severity": 0, 
+        "severity_value": 0.0584, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmony performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: isolated_label_asw\n  Worst score: -0.0584%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmony isolated_label_asw", 
+        "value": 0.5298, 
+        "severity": 0, 
+        "severity_value": 0.2649, 
+        "code": "best_score <= 2", 
+        "message": "Method harmony performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: isolated_label_asw\n  Best score: 0.5298%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmonypy isolated_label_asw", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmonypy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: isolated_label_asw\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmonypy isolated_label_asw", 
+        "value": 0.5225, 
+        "severity": 0, 
+        "severity_value": 0.26125, 
+        "code": "best_score <= 2", 
+        "message": "Method harmonypy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: isolated_label_asw\n  Best score: 0.5225%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score liger isolated_label_asw", 
+        "value": -0.1809, 
+        "severity": 0, 
+        "severity_value": 0.1809, 
+        "code": "worst_score >= -1", 
+        "message": "Method liger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: isolated_label_asw\n  Worst score: -0.1809%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score liger isolated_label_asw", 
+        "value": 0.3881, 
+        "severity": 0, 
+        "severity_value": 0.19405, 
+        "code": "best_score <= 2", 
+        "message": "Method liger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: isolated_label_asw\n  Best score: 0.3881%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score mnnpy isolated_label_asw", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method mnnpy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: isolated_label_asw\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score mnnpy isolated_label_asw", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method mnnpy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: isolated_label_asw\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score pyliger isolated_label_asw", 
+        "value": -0.0862, 
+        "severity": 0, 
+        "severity_value": 0.0862, 
+        "code": "worst_score >= -1", 
+        "message": "Method pyliger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: isolated_label_asw\n  Worst score: -0.0862%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score pyliger isolated_label_asw", 
+        "value": 0.3914, 
+        "severity": 0, 
+        "severity_value": 0.1957, 
+        "code": "best_score <= 2", 
+        "message": "Method pyliger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: isolated_label_asw\n  Best score: 0.3914%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scalex isolated_label_asw", 
+        "value": -0.1188, 
+        "severity": 0, 
+        "severity_value": 0.1188, 
+        "code": "worst_score >= -1", 
+        "message": "Method scalex performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: isolated_label_asw\n  Worst score: -0.1188%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scalex isolated_label_asw", 
+        "value": 0.4316, 
+        "severity": 0, 
+        "severity_value": 0.2158, 
+        "code": "best_score <= 2", 
+        "message": "Method scalex performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: isolated_label_asw\n  Best score: 0.4316%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanorama isolated_label_asw", 
+        "value": -0.32, 
+        "severity": 0, 
+        "severity_value": 0.32, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanorama performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: isolated_label_asw\n  Worst score: -0.32%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanorama isolated_label_asw", 
+        "value": 0.1627, 
+        "severity": 0, 
+        "severity_value": 0.08135, 
+        "code": "best_score <= 2", 
+        "message": "Method scanorama performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: isolated_label_asw\n  Best score: 0.1627%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanvi isolated_label_asw", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: isolated_label_asw\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanvi isolated_label_asw", 
+        "value": 0.5566, 
+        "severity": 0, 
+        "severity_value": 0.2783, 
+        "code": "best_score <= 2", 
+        "message": "Method scanvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: isolated_label_asw\n  Best score: 0.5566%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_finetuned isolated_label_asw", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_finetuned performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: isolated_label_asw\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_finetuned isolated_label_asw", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_finetuned performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: isolated_label_asw\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_zeroshot isolated_label_asw", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_zeroshot performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: isolated_label_asw\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_zeroshot isolated_label_asw", 
+        "value": 0.1915, 
+        "severity": 0, 
+        "severity_value": 0.09575, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_zeroshot performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: isolated_label_asw\n  Best score: 0.1915%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scimilarity isolated_label_asw", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scimilarity performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: isolated_label_asw\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scimilarity isolated_label_asw", 
+        "value": 0.3993, 
+        "severity": 0, 
+        "severity_value": 0.19965, 
+        "code": "best_score <= 2", 
+        "message": "Method scimilarity performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: isolated_label_asw\n  Best score: 0.3993%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scprint isolated_label_asw", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: isolated_label_asw\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scprint isolated_label_asw", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: isolated_label_asw\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scvi isolated_label_asw", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: isolated_label_asw\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scvi isolated_label_asw", 
+        "value": 0.5337, 
+        "severity": 0, 
+        "severity_value": 0.26685, 
+        "code": "best_score <= 2", 
+        "message": "Method scvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: isolated_label_asw\n  Best score: 0.5337%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score uce isolated_label_asw", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method uce performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: isolated_label_asw\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score uce isolated_label_asw", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method uce performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: isolated_label_asw\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types isolated_label_f1", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: isolated_label_f1\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types isolated_label_f1", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: isolated_label_f1\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types_jittered isolated_label_f1", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types_jittered performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: isolated_label_f1\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types_jittered isolated_label_f1", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types_jittered performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: isolated_label_f1\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration isolated_label_f1", 
+        "value": 0.8931, 
+        "severity": 0, 
+        "severity_value": 0.44655, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: isolated_label_f1\n  Best score: 0.8931%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration_batch isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration_batch isolated_label_f1", 
+        "value": 0.1708, 
+        "severity": 0, 
+        "severity_value": 0.0854, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: isolated_label_f1\n  Best score: 0.1708%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration isolated_label_f1", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: isolated_label_f1\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration isolated_label_f1", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: isolated_label_f1\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_batch isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_batch isolated_label_f1", 
+        "value": 0.0088, 
+        "severity": 0, 
+        "severity_value": 0.0044, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: isolated_label_f1\n  Best score: 0.0088%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_cell_type isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_cell_type isolated_label_f1", 
+        "value": 0.8923, 
+        "severity": 0, 
+        "severity_value": 0.44615, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: isolated_label_f1\n  Best score: 0.8923%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_fastmnn isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_fastmnn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_fastmnn isolated_label_f1", 
+        "value": 0.8558, 
+        "severity": 0, 
+        "severity_value": 0.4279, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_fastmnn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: isolated_label_f1\n  Best score: 0.8558%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_mnn_correct isolated_label_f1", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_mnn_correct performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: isolated_label_f1\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_mnn_correct isolated_label_f1", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_mnn_correct performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: isolated_label_f1\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score bbknn isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method bbknn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score bbknn isolated_label_f1", 
+        "value": 0.0747, 
+        "severity": 0, 
+        "severity_value": 0.03735, 
+        "code": "best_score <= 2", 
+        "message": "Method bbknn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: isolated_label_f1\n  Best score: 0.0747%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score combat isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method combat performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score combat isolated_label_f1", 
+        "value": 0.7629, 
+        "severity": 0, 
+        "severity_value": 0.38145, 
+        "code": "best_score <= 2", 
+        "message": "Method combat performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: isolated_label_f1\n  Best score: 0.7629%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score geneformer isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method geneformer performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score geneformer isolated_label_f1", 
+        "value": 0.0766, 
+        "severity": 0, 
+        "severity_value": 0.0383, 
+        "code": "best_score <= 2", 
+        "message": "Method geneformer performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: isolated_label_f1\n  Best score: 0.0766%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmony isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmony performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmony isolated_label_f1", 
+        "value": 0.0793, 
+        "severity": 0, 
+        "severity_value": 0.03965, 
+        "code": "best_score <= 2", 
+        "message": "Method harmony performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: isolated_label_f1\n  Best score: 0.0793%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmonypy isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmonypy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmonypy isolated_label_f1", 
+        "value": 0.0884, 
+        "severity": 0, 
+        "severity_value": 0.0442, 
+        "code": "best_score <= 2", 
+        "message": "Method harmonypy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: isolated_label_f1\n  Best score: 0.0884%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score liger isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method liger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score liger isolated_label_f1", 
+        "value": 0.1195, 
+        "severity": 0, 
+        "severity_value": 0.05975, 
+        "code": "best_score <= 2", 
+        "message": "Method liger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: isolated_label_f1\n  Best score: 0.1195%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score mnnpy isolated_label_f1", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method mnnpy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: isolated_label_f1\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score mnnpy isolated_label_f1", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method mnnpy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: isolated_label_f1\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score pyliger isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method pyliger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score pyliger isolated_label_f1", 
+        "value": 0.1265, 
+        "severity": 0, 
+        "severity_value": 0.06325, 
+        "code": "best_score <= 2", 
+        "message": "Method pyliger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: isolated_label_f1\n  Best score: 0.1265%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scalex isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scalex performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scalex isolated_label_f1", 
+        "value": 0.1229, 
+        "severity": 0, 
+        "severity_value": 0.06145, 
+        "code": "best_score <= 2", 
+        "message": "Method scalex performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: isolated_label_f1\n  Best score: 0.1229%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanorama isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanorama performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanorama isolated_label_f1", 
+        "value": 0.04, 
+        "severity": 0, 
+        "severity_value": 0.02, 
+        "code": "best_score <= 2", 
+        "message": "Method scanorama performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: isolated_label_f1\n  Best score: 0.04%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanvi isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanvi isolated_label_f1", 
+        "value": 0.9613, 
+        "severity": 0, 
+        "severity_value": 0.48065, 
+        "code": "best_score <= 2", 
+        "message": "Method scanvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: isolated_label_f1\n  Best score: 0.9613%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_finetuned isolated_label_f1", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_finetuned performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: isolated_label_f1\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_finetuned isolated_label_f1", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_finetuned performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: isolated_label_f1\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_zeroshot isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_zeroshot performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_zeroshot isolated_label_f1", 
+        "value": 0.0686, 
+        "severity": 0, 
+        "severity_value": 0.0343, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_zeroshot performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: isolated_label_f1\n  Best score: 0.0686%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scimilarity isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scimilarity performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scimilarity isolated_label_f1", 
+        "value": 0.842, 
+        "severity": 0, 
+        "severity_value": 0.421, 
+        "code": "best_score <= 2", 
+        "message": "Method scimilarity performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: isolated_label_f1\n  Best score: 0.842%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scprint isolated_label_f1", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: isolated_label_f1\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scprint isolated_label_f1", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: isolated_label_f1\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scvi isolated_label_f1", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: isolated_label_f1\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scvi isolated_label_f1", 
+        "value": 0.9144, 
+        "severity": 0, 
+        "severity_value": 0.4572, 
+        "code": "best_score <= 2", 
+        "message": "Method scvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: isolated_label_f1\n  Best score: 0.9144%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score uce isolated_label_f1", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method uce performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: isolated_label_f1\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score uce isolated_label_f1", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method uce performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: isolated_label_f1\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types kbet", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: kbet\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types_jittered kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types_jittered performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types_jittered kbet", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types_jittered performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: kbet\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration kbet", 
+        "value": 0.3656, 
+        "severity": 0, 
+        "severity_value": 0.1828, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: kbet\n  Best score: 0.3656%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration_batch kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration_batch kbet", 
+        "value": 0.5813, 
+        "severity": 0, 
+        "severity_value": 0.29065, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: kbet\n  Best score: 0.5813%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration kbet", 
+        "value": 0.35, 
+        "severity": 0, 
+        "severity_value": 0.175, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: kbet\n  Best score: 0.35%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_batch kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_batch kbet", 
+        "value": 0.0472, 
+        "severity": 0, 
+        "severity_value": 0.0236, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: kbet\n  Best score: 0.0472%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_cell_type kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_cell_type kbet", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: kbet\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_fastmnn kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_fastmnn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_fastmnn kbet", 
+        "value": 0.4201, 
+        "severity": 0, 
+        "severity_value": 0.21005, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_fastmnn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: kbet\n  Best score: 0.4201%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_mnn_correct kbet", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_mnn_correct performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: kbet\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_mnn_correct kbet", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_mnn_correct performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: kbet\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score bbknn kbet", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method bbknn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: kbet\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score bbknn kbet", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method bbknn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: kbet\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score combat kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method combat performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score combat kbet", 
+        "value": 0.2965, 
+        "severity": 0, 
+        "severity_value": 0.14825, 
+        "code": "best_score <= 2", 
+        "message": "Method combat performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: kbet\n  Best score: 0.2965%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score geneformer kbet", 
+        "value": -0.0303, 
+        "severity": 0, 
+        "severity_value": 0.0303, 
+        "code": "worst_score >= -1", 
+        "message": "Method geneformer performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: kbet\n  Worst score: -0.0303%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score geneformer kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method geneformer performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: kbet\n  Best score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmony kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmony performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmony kbet", 
+        "value": 0.4381, 
+        "severity": 0, 
+        "severity_value": 0.21905, 
+        "code": "best_score <= 2", 
+        "message": "Method harmony performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: kbet\n  Best score: 0.4381%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmonypy kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmonypy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmonypy kbet", 
+        "value": 0.43, 
+        "severity": 0, 
+        "severity_value": 0.215, 
+        "code": "best_score <= 2", 
+        "message": "Method harmonypy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: kbet\n  Best score: 0.43%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score liger kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method liger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score liger kbet", 
+        "value": 0.4092, 
+        "severity": 0, 
+        "severity_value": 0.2046, 
+        "code": "best_score <= 2", 
+        "message": "Method liger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: kbet\n  Best score: 0.4092%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score mnnpy kbet", 
+        "value": -0.0179, 
+        "severity": 0, 
+        "severity_value": 0.0179, 
+        "code": "worst_score >= -1", 
+        "message": "Method mnnpy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: kbet\n  Worst score: -0.0179%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score mnnpy kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method mnnpy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: kbet\n  Best score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score pyliger kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method pyliger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score pyliger kbet", 
+        "value": 0.8009, 
+        "severity": 0, 
+        "severity_value": 0.40045, 
+        "code": "best_score <= 2", 
+        "message": "Method pyliger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: kbet\n  Best score: 0.8009%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scalex kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scalex performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scalex kbet", 
+        "value": 0.3316, 
+        "severity": 0, 
+        "severity_value": 0.1658, 
+        "code": "best_score <= 2", 
+        "message": "Method scalex performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: kbet\n  Best score: 0.3316%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanorama kbet", 
+        "value": -0.0178, 
+        "severity": 0, 
+        "severity_value": 0.0178, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanorama performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: kbet\n  Worst score: -0.0178%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanorama kbet", 
+        "value": 0.0358, 
+        "severity": 0, 
+        "severity_value": 0.0179, 
+        "code": "best_score <= 2", 
+        "message": "Method scanorama performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: kbet\n  Best score: 0.0358%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanvi kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanvi kbet", 
+        "value": 0.3767, 
+        "severity": 0, 
+        "severity_value": 0.18835, 
+        "code": "best_score <= 2", 
+        "message": "Method scanvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: kbet\n  Best score: 0.3767%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_finetuned kbet", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_finetuned performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: kbet\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_finetuned kbet", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_finetuned performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: kbet\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_zeroshot kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_zeroshot performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_zeroshot kbet", 
+        "value": 0.3614, 
+        "severity": 0, 
+        "severity_value": 0.1807, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_zeroshot performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: kbet\n  Best score: 0.3614%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scimilarity kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scimilarity performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scimilarity kbet", 
+        "value": 0.371, 
+        "severity": 0, 
+        "severity_value": 0.1855, 
+        "code": "best_score <= 2", 
+        "message": "Method scimilarity performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: kbet\n  Best score: 0.371%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scprint kbet", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: kbet\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scprint kbet", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: kbet\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scvi kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scvi kbet", 
+        "value": 0.3572, 
+        "severity": 0, 
+        "severity_value": 0.1786, 
+        "code": "best_score <= 2", 
+        "message": "Method scvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: kbet\n  Best score: 0.3572%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score uce kbet", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method uce performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: kbet\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score uce kbet", 
+        "value": 0.2085, 
+        "severity": 0, 
+        "severity_value": 0.10425, 
+        "code": "best_score <= 2", 
+        "message": "Method uce performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: kbet\n  Best score: 0.2085%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types ilisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: ilisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types ilisi", 
+        "value": 0.9505, 
+        "severity": 0, 
+        "severity_value": 0.47525, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: ilisi\n  Best score: 0.9505%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types_jittered ilisi", 
+        "value": 0.3772, 
+        "severity": 0, 
+        "severity_value": -0.3772, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types_jittered performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: ilisi\n  Worst score: 0.3772%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types_jittered ilisi", 
+        "value": 0.9547, 
+        "severity": 0, 
+        "severity_value": 0.47735, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types_jittered performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: ilisi\n  Best score: 0.9547%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration ilisi", 
+        "value": 1.9396e-15, 
+        "severity": 0, 
+        "severity_value": -1.9396e-15, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: ilisi\n  Worst score: 1.9396e-15%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration ilisi", 
+        "value": 0.1426, 
+        "severity": 0, 
+        "severity_value": 0.0713, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: ilisi\n  Best score: 0.1426%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration_batch ilisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: ilisi\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration_batch ilisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: ilisi\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration ilisi", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": -1.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: ilisi\n  Worst score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration ilisi", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: ilisi\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_batch ilisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: ilisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_batch ilisi", 
+        "value": 0.1058, 
+        "severity": 0, 
+        "severity_value": 0.0529, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: ilisi\n  Best score: 0.1058%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_cell_type ilisi", 
+        "value": 0.4015, 
+        "severity": 0, 
+        "severity_value": -0.4015, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: ilisi\n  Worst score: 0.4015%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_cell_type ilisi", 
+        "value": 0.9642, 
+        "severity": 0, 
+        "severity_value": 0.4821, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: ilisi\n  Best score: 0.9642%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_fastmnn ilisi", 
+        "value": 0.1066, 
+        "severity": 0, 
+        "severity_value": -0.1066, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_fastmnn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: ilisi\n  Worst score: 0.1066%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_fastmnn ilisi", 
+        "value": 0.5562, 
+        "severity": 0, 
+        "severity_value": 0.2781, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_fastmnn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: ilisi\n  Best score: 0.5562%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_mnn_correct ilisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_mnn_correct performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: ilisi\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_mnn_correct ilisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_mnn_correct performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: ilisi\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score bbknn ilisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method bbknn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: ilisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score bbknn ilisi", 
+        "value": 36.1009, 
+        "severity": 3, 
+        "severity_value": 18.05045, 
+        "code": "best_score <= 2", 
+        "message": "Method bbknn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: ilisi\n  Best score: 36.1009%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score combat ilisi", 
+        "value": 1.9396e-15, 
+        "severity": 0, 
+        "severity_value": -1.9396e-15, 
+        "code": "worst_score >= -1", 
+        "message": "Method combat performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: ilisi\n  Worst score: 1.9396e-15%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score combat ilisi", 
+        "value": 0.3299, 
+        "severity": 0, 
+        "severity_value": 0.16495, 
+        "code": "best_score <= 2", 
+        "message": "Method combat performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: ilisi\n  Best score: 0.3299%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score geneformer ilisi", 
+        "value": -0.0015, 
+        "severity": 0, 
+        "severity_value": 0.0015, 
+        "code": "worst_score >= -1", 
+        "message": "Method geneformer performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: ilisi\n  Worst score: -0.0015%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score geneformer ilisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method geneformer performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: ilisi\n  Best score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmony ilisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmony performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: ilisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmony ilisi", 
+        "value": 0.6884, 
+        "severity": 0, 
+        "severity_value": 0.3442, 
+        "code": "best_score <= 2", 
+        "message": "Method harmony performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: ilisi\n  Best score: 0.6884%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmonypy ilisi", 
+        "value": 0.134, 
+        "severity": 0, 
+        "severity_value": -0.134, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmonypy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: ilisi\n  Worst score: 0.134%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmonypy ilisi", 
+        "value": 0.6824, 
+        "severity": 0, 
+        "severity_value": 0.3412, 
+        "code": "best_score <= 2", 
+        "message": "Method harmonypy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: ilisi\n  Best score: 0.6824%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score liger ilisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method liger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: ilisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score liger ilisi", 
+        "value": 0.7783, 
+        "severity": 0, 
+        "severity_value": 0.38915, 
+        "code": "best_score <= 2", 
+        "message": "Method liger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: ilisi\n  Best score: 0.7783%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score mnnpy ilisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method mnnpy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: ilisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score mnnpy ilisi", 
+        "value": 0.3527, 
+        "severity": 0, 
+        "severity_value": 0.17635, 
+        "code": "best_score <= 2", 
+        "message": "Method mnnpy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: ilisi\n  Best score: 0.3527%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score pyliger ilisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method pyliger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: ilisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score pyliger ilisi", 
+        "value": 0.8751, 
+        "severity": 0, 
+        "severity_value": 0.43755, 
+        "code": "best_score <= 2", 
+        "message": "Method pyliger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: ilisi\n  Best score: 0.8751%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scalex ilisi", 
+        "value": 3.8792e-15, 
+        "severity": 0, 
+        "severity_value": -3.8792e-15, 
+        "code": "worst_score >= -1", 
+        "message": "Method scalex performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: ilisi\n  Worst score: 3.8792e-15%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scalex ilisi", 
+        "value": 0.6444, 
+        "severity": 0, 
+        "severity_value": 0.3222, 
+        "code": "best_score <= 2", 
+        "message": "Method scalex performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: ilisi\n  Best score: 0.6444%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanorama ilisi", 
+        "value": 1.9396e-15, 
+        "severity": 0, 
+        "severity_value": -1.9396e-15, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanorama performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: ilisi\n  Worst score: 1.9396e-15%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanorama ilisi", 
+        "value": 0.9722, 
+        "severity": 0, 
+        "severity_value": 0.4861, 
+        "code": "best_score <= 2", 
+        "message": "Method scanorama performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: ilisi\n  Best score: 0.9722%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanvi ilisi", 
+        "value": 3.8792e-15, 
+        "severity": 0, 
+        "severity_value": -3.8792e-15, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: ilisi\n  Worst score: 3.8792e-15%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanvi ilisi", 
+        "value": 0.6433, 
+        "severity": 0, 
+        "severity_value": 0.32165, 
+        "code": "best_score <= 2", 
+        "message": "Method scanvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: ilisi\n  Best score: 0.6433%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_finetuned ilisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_finetuned performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: ilisi\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_finetuned ilisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_finetuned performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: ilisi\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_zeroshot ilisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_zeroshot performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: ilisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_zeroshot ilisi", 
+        "value": 0.4652, 
+        "severity": 0, 
+        "severity_value": 0.2326, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_zeroshot performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: ilisi\n  Best score: 0.4652%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scimilarity ilisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scimilarity performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: ilisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scimilarity ilisi", 
+        "value": 0.4672, 
+        "severity": 0, 
+        "severity_value": 0.2336, 
+        "code": "best_score <= 2", 
+        "message": "Method scimilarity performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: ilisi\n  Best score: 0.4672%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scprint ilisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: ilisi\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scprint ilisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: ilisi\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scvi ilisi", 
+        "value": 3.8792e-15, 
+        "severity": 0, 
+        "severity_value": -3.8792e-15, 
+        "code": "worst_score >= -1", 
+        "message": "Method scvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: ilisi\n  Worst score: 3.8792e-15%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scvi ilisi", 
+        "value": 0.6505, 
+        "severity": 0, 
+        "severity_value": 0.32525, 
+        "code": "best_score <= 2", 
+        "message": "Method scvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: ilisi\n  Best score: 0.6505%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score uce ilisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method uce performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: ilisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score uce ilisi", 
+        "value": 0.4688, 
+        "severity": 0, 
+        "severity_value": 0.2344, 
+        "code": "best_score <= 2", 
+        "message": "Method uce performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: ilisi\n  Best score: 0.4688%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types clisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: clisi\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types clisi", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: clisi\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types_jittered clisi", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": -1.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types_jittered performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: clisi\n  Worst score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types_jittered clisi", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types_jittered performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: clisi\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration clisi", 
+        "value": 0.9787, 
+        "severity": 0, 
+        "severity_value": -0.9787, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: clisi\n  Worst score: 0.9787%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration clisi", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: clisi\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration_batch clisi", 
+        "value": 0.9727, 
+        "severity": 0, 
+        "severity_value": -0.9727, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: clisi\n  Worst score: 0.9727%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration_batch clisi", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: clisi\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration clisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: clisi\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration clisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: clisi\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_batch clisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: clisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_batch clisi", 
+        "value": 0.4232, 
+        "severity": 0, 
+        "severity_value": 0.2116, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: clisi\n  Best score: 0.4232%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_cell_type clisi", 
+        "value": 0.9787, 
+        "severity": 0, 
+        "severity_value": -0.9787, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: clisi\n  Worst score: 0.9787%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_cell_type clisi", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: clisi\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_fastmnn clisi", 
+        "value": 0.9545, 
+        "severity": 0, 
+        "severity_value": -0.9545, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_fastmnn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: clisi\n  Worst score: 0.9545%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_fastmnn clisi", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_fastmnn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: clisi\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_mnn_correct clisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_mnn_correct performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: clisi\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_mnn_correct clisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_mnn_correct performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: clisi\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score bbknn clisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method bbknn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: clisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score bbknn clisi", 
+        "value": 0.8101, 
+        "severity": 0, 
+        "severity_value": 0.40505, 
+        "code": "best_score <= 2", 
+        "message": "Method bbknn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: clisi\n  Best score: 0.8101%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score combat clisi", 
+        "value": 0.9761, 
+        "severity": 0, 
+        "severity_value": -0.9761, 
+        "code": "worst_score >= -1", 
+        "message": "Method combat performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: clisi\n  Worst score: 0.9761%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score combat clisi", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method combat performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: clisi\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score geneformer clisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method geneformer performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: clisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score geneformer clisi", 
+        "value": 0.7421, 
+        "severity": 0, 
+        "severity_value": 0.37105, 
+        "code": "best_score <= 2", 
+        "message": "Method geneformer performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: clisi\n  Best score: 0.7421%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmony clisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmony performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: clisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmony clisi", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method harmony performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: clisi\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmonypy clisi", 
+        "value": 0.954, 
+        "severity": 0, 
+        "severity_value": -0.954, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmonypy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: clisi\n  Worst score: 0.954%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmonypy clisi", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method harmonypy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: clisi\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score liger clisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method liger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: clisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score liger clisi", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method liger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: clisi\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score mnnpy clisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method mnnpy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: clisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score mnnpy clisi", 
+        "value": 0.6004, 
+        "severity": 0, 
+        "severity_value": 0.3002, 
+        "code": "best_score <= 2", 
+        "message": "Method mnnpy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: clisi\n  Best score: 0.6004%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score pyliger clisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method pyliger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: clisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score pyliger clisi", 
+        "value": 0.9982, 
+        "severity": 0, 
+        "severity_value": 0.4991, 
+        "code": "best_score <= 2", 
+        "message": "Method pyliger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: clisi\n  Best score: 0.9982%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scalex clisi", 
+        "value": 0.8937, 
+        "severity": 0, 
+        "severity_value": -0.8937, 
+        "code": "worst_score >= -1", 
+        "message": "Method scalex performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: clisi\n  Worst score: 0.8937%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scalex clisi", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method scalex performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: clisi\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanorama clisi", 
+        "value": 0.0195, 
+        "severity": 0, 
+        "severity_value": -0.0195, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanorama performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: clisi\n  Worst score: 0.0195%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanorama clisi", 
+        "value": 0.5756, 
+        "severity": 0, 
+        "severity_value": 0.2878, 
+        "code": "best_score <= 2", 
+        "message": "Method scanorama performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: clisi\n  Best score: 0.5756%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanvi clisi", 
+        "value": 0.9983, 
+        "severity": 0, 
+        "severity_value": -0.9983, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: clisi\n  Worst score: 0.9983%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanvi clisi", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method scanvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: clisi\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_finetuned clisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_finetuned performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: clisi\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_finetuned clisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_finetuned performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: clisi\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_zeroshot clisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_zeroshot performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: clisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_zeroshot clisi", 
+        "value": 0.9973, 
+        "severity": 0, 
+        "severity_value": 0.49865, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_zeroshot performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: clisi\n  Best score: 0.9973%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scimilarity clisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scimilarity performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: clisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scimilarity clisi", 
+        "value": 0.9989, 
+        "severity": 0, 
+        "severity_value": 0.49945, 
+        "code": "best_score <= 2", 
+        "message": "Method scimilarity performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: clisi\n  Best score: 0.9989%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scprint clisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: clisi\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scprint clisi", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: clisi\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scvi clisi", 
+        "value": 0.9852, 
+        "severity": 0, 
+        "severity_value": -0.9852, 
+        "code": "worst_score >= -1", 
+        "message": "Method scvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: clisi\n  Worst score: 0.9852%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scvi clisi", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method scvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: clisi\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score uce clisi", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method uce performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: clisi\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score uce clisi", 
+        "value": 0.9973, 
+        "severity": 0, 
+        "severity_value": 0.49865, 
+        "code": "best_score <= 2", 
+        "message": "Method uce performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: clisi\n  Best score: 0.9973%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types pcr", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: pcr\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types pcr", 
+        "value": 0.7115, 
+        "severity": 0, 
+        "severity_value": 0.35575, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types\n  Metric id: pcr\n  Best score: 0.7115%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score embed_cell_types_jittered pcr", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method embed_cell_types_jittered performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: pcr\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score embed_cell_types_jittered pcr", 
+        "value": 0.7116, 
+        "severity": 0, 
+        "severity_value": 0.3558, 
+        "code": "best_score <= 2", 
+        "message": "Method embed_cell_types_jittered performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: embed_cell_types_jittered\n  Metric id: pcr\n  Best score: 0.7116%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration pcr", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: pcr\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration pcr", 
+        "value": 5.5861e-07, 
+        "severity": 0, 
+        "severity_value": 2.79305e-07, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration\n  Metric id: pcr\n  Best score: 5.5861e-07%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score no_integration_batch pcr", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": -1.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: pcr\n  Worst score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score no_integration_batch pcr", 
+        "value": 1, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: no_integration_batch\n  Metric id: pcr\n  Best score: 1%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration pcr", 
+        "value": 0.9966, 
+        "severity": 0, 
+        "severity_value": -0.9966, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: pcr\n  Worst score: 0.9966%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration pcr", 
+        "value": 0.9997, 
+        "severity": 0, 
+        "severity_value": 0.49985, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration\n  Metric id: pcr\n  Best score: 0.9997%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_batch pcr", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_batch performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: pcr\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_batch pcr", 
+        "value": 1.571e-06, 
+        "severity": 0, 
+        "severity_value": 7.855e-07, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_batch performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_batch\n  Metric id: pcr\n  Best score: 1.571e-06%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score shuffle_integration_by_cell_type pcr", 
+        "value": 0.2357, 
+        "severity": 0, 
+        "severity_value": -0.2357, 
+        "code": "worst_score >= -1", 
+        "message": "Method shuffle_integration_by_cell_type performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: pcr\n  Worst score: 0.2357%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score shuffle_integration_by_cell_type pcr", 
+        "value": 0.7849, 
+        "severity": 0, 
+        "severity_value": 0.39245, 
+        "code": "best_score <= 2", 
+        "message": "Method shuffle_integration_by_cell_type performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: shuffle_integration_by_cell_type\n  Metric id: pcr\n  Best score: 0.7849%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_fastmnn pcr", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_fastmnn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: pcr\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_fastmnn pcr", 
+        "value": 0.5337, 
+        "severity": 0, 
+        "severity_value": 0.26685, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_fastmnn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_fastmnn\n  Metric id: pcr\n  Best score: 0.5337%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score batchelor_mnn_correct pcr", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method batchelor_mnn_correct performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: pcr\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score batchelor_mnn_correct pcr", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method batchelor_mnn_correct performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: batchelor_mnn_correct\n  Metric id: pcr\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score bbknn pcr", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method bbknn performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: pcr\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score bbknn pcr", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method bbknn performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: bbknn\n  Metric id: pcr\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score combat pcr", 
+        "value": 0.9996, 
+        "severity": 0, 
+        "severity_value": -0.9996, 
+        "code": "worst_score >= -1", 
+        "message": "Method combat performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: pcr\n  Worst score: 0.9996%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score combat pcr", 
+        "value": 1.0, 
+        "severity": 0, 
+        "severity_value": 0.5, 
+        "code": "best_score <= 2", 
+        "message": "Method combat performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: combat\n  Metric id: pcr\n  Best score: 1.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score geneformer pcr", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method geneformer performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: pcr\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score geneformer pcr", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method geneformer performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: geneformer\n  Metric id: pcr\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmony pcr", 
+        "value": 0.4488, 
+        "severity": 0, 
+        "severity_value": -0.4488, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmony performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: pcr\n  Worst score: 0.4488%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmony pcr", 
+        "value": 0.6812, 
+        "severity": 0, 
+        "severity_value": 0.3406, 
+        "code": "best_score <= 2", 
+        "message": "Method harmony performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmony\n  Metric id: pcr\n  Best score: 0.6812%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score harmonypy pcr", 
+        "value": 0.4441, 
+        "severity": 0, 
+        "severity_value": -0.4441, 
+        "code": "worst_score >= -1", 
+        "message": "Method harmonypy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: pcr\n  Worst score: 0.4441%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score harmonypy pcr", 
+        "value": 0.6897, 
+        "severity": 0, 
+        "severity_value": 0.34485, 
+        "code": "best_score <= 2", 
+        "message": "Method harmonypy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: harmonypy\n  Metric id: pcr\n  Best score: 0.6897%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score liger pcr", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method liger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: pcr\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score liger pcr", 
+        "value": 0.8296, 
+        "severity": 0, 
+        "severity_value": 0.4148, 
+        "code": "best_score <= 2", 
+        "message": "Method liger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: liger\n  Metric id: pcr\n  Best score: 0.8296%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score mnnpy pcr", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method mnnpy performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: pcr\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score mnnpy pcr", 
+        "value": 0.6494, 
+        "severity": 0, 
+        "severity_value": 0.3247, 
+        "code": "best_score <= 2", 
+        "message": "Method mnnpy performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: mnnpy\n  Metric id: pcr\n  Best score: 0.6494%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score pyliger pcr", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method pyliger performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: pcr\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score pyliger pcr", 
+        "value": 0.8038, 
+        "severity": 0, 
+        "severity_value": 0.4019, 
+        "code": "best_score <= 2", 
+        "message": "Method pyliger performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: pyliger\n  Metric id: pcr\n  Best score: 0.8038%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scalex pcr", 
+        "value": 0.9785, 
+        "severity": 0, 
+        "severity_value": -0.9785, 
+        "code": "worst_score >= -1", 
+        "message": "Method scalex performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: pcr\n  Worst score: 0.9785%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scalex pcr", 
+        "value": 0.9999, 
+        "severity": 0, 
+        "severity_value": 0.49995, 
+        "code": "best_score <= 2", 
+        "message": "Method scalex performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scalex\n  Metric id: pcr\n  Best score: 0.9999%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanorama pcr", 
+        "value": 0.003, 
+        "severity": 0, 
+        "severity_value": -0.003, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanorama performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: pcr\n  Worst score: 0.003%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanorama pcr", 
+        "value": 0.9994, 
+        "severity": 0, 
+        "severity_value": 0.4997, 
+        "code": "best_score <= 2", 
+        "message": "Method scanorama performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanorama\n  Metric id: pcr\n  Best score: 0.9994%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scanvi pcr", 
+        "value": 0.0347, 
+        "severity": 0, 
+        "severity_value": -0.0347, 
+        "code": "worst_score >= -1", 
+        "message": "Method scanvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: pcr\n  Worst score: 0.0347%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scanvi pcr", 
+        "value": 0.636, 
+        "severity": 0, 
+        "severity_value": 0.318, 
+        "code": "best_score <= 2", 
+        "message": "Method scanvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scanvi\n  Metric id: pcr\n  Best score: 0.636%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_finetuned pcr", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_finetuned performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: pcr\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_finetuned pcr", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_finetuned performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_finetuned\n  Metric id: pcr\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scgpt_zeroshot pcr", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scgpt_zeroshot performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: pcr\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scgpt_zeroshot pcr", 
+        "value": 0.1917, 
+        "severity": 0, 
+        "severity_value": 0.09585, 
+        "code": "best_score <= 2", 
+        "message": "Method scgpt_zeroshot performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scgpt_zeroshot\n  Metric id: pcr\n  Best score: 0.1917%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scimilarity pcr", 
+        "value": 0.0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scimilarity performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: pcr\n  Worst score: 0.0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scimilarity pcr", 
+        "value": 0.3969, 
+        "severity": 0, 
+        "severity_value": 0.19845, 
+        "code": "best_score <= 2", 
+        "message": "Method scimilarity performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scimilarity\n  Metric id: pcr\n  Best score: 0.3969%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scprint pcr", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: pcr\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scprint pcr", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scprint\n  Metric id: pcr\n  Best score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score scvi pcr", 
+        "value": 0.2525, 
+        "severity": 0, 
+        "severity_value": -0.2525, 
+        "code": "worst_score >= -1", 
+        "message": "Method scvi performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: pcr\n  Worst score: 0.2525%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score scvi pcr", 
+        "value": 0.681, 
+        "severity": 0, 
+        "severity_value": 0.3405, 
+        "code": "best_score <= 2", 
+        "message": "Method scvi performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: scvi\n  Metric id: pcr\n  Best score: 0.681%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Worst score uce pcr", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": -0.0, 
+        "code": "worst_score >= -1", 
+        "message": "Method uce performs much worse than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: pcr\n  Worst score: 0%\n"
+    }, 
+    {
+        "task_id": "task_batch_integration", 
+        "category": "Scaling", 
+        "name": "Best score uce pcr", 
+        "value": 0, 
+        "severity": 0, 
+        "severity_value": 0.0, 
+        "code": "best_score <= 2", 
+        "message": "Method uce performs a lot better than baselines.\n  Task id: task_batch_integration\n  Method id: uce\n  Metric id: pcr\n  Best score: 0%\n"
+    }
+]
\ No newline at end of file
diff --git a/results/batch_integration/data/results.json b/results/batch_integration/data/results.json
new file mode 100644
index 00000000..83118f5a
--- /dev/null
+++ b/results/batch_integration/data/results.json
@@ -0,0 +1,6866 @@
+[
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "batchelor_fastmnn",
+    "metric_values": {
+      "ari": 0.7601,
+      "asw_batch": 0.894,
+      "asw_label": 0.6657,
+      "cell_cycle_conservation": 0.8574,
+      "clisi": 0.9999,
+      "graph_connectivity": 0.9736,
+      "hvg_overlap": "NA",
+      "ilisi": 0.2719,
+      "isolated_label_asw": "NA",
+      "isolated_label_f1": "NA",
+      "kbet": 0.378,
+      "nmi": 0.8274,
+      "pcr": 0.5337
+    },
+    "scaled_scores": {
+      "ari": 0.7601,
+      "asw_batch": 0.7401,
+      "asw_label": 0.3524,
+      "cell_cycle_conservation": 0.9955,
+      "clisi": 0.9997,
+      "graph_connectivity": 0.965,
+      "hvg_overlap": 0,
+      "ilisi": 0.5562,
+      "isolated_label_asw": 0,
+      "isolated_label_f1": 0,
+      "kbet": 0.3868,
+      "nmi": 0.8271,
+      "pcr": 0.5337
+    },
+    "mean_score": 0.5474,
+    "resources": {
+      "submit": "2025-01-20 15:02:22",
+      "exit_code": 0,
+      "duration_sec": 85,
+      "cpu_pct": 116.4,
+      "peak_memory_mb": 7168,
+      "disk_read_mb": 116,
+      "disk_write_mb": 16
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "bbknn",
+    "metric_values": {
+      "ari": "NA",
+      "asw_batch": "NA",
+      "asw_label": "NA",
+      "cell_cycle_conservation": "NA",
+      "clisi": "NA",
+      "graph_connectivity": 0.984,
+      "hvg_overlap": "NA",
+      "ilisi": "NA",
+      "isolated_label_asw": "NA",
+      "isolated_label_f1": "NA",
+      "kbet": "NA",
+      "nmi": "NA",
+      "pcr": "NA"
+    },
+    "scaled_scores": {
+      "ari": 0,
+      "asw_batch": 0,
+      "asw_label": 0,
+      "cell_cycle_conservation": 0,
+      "clisi": 0,
+      "graph_connectivity": 0.9788,
+      "hvg_overlap": 0,
+      "ilisi": 0,
+      "isolated_label_asw": 0,
+      "isolated_label_f1": 0,
+      "kbet": 0,
+      "nmi": 0,
+      "pcr": 0
+    },
+    "mean_score": 0.0753,
+    "resources": {
+      "submit": "2025-01-20 15:02:22",
+      "exit_code": 0,
+      "duration_sec": 63,
+      "cpu_pct": 1136.8,
+      "peak_memory_mb": 12186,
+      "disk_read_mb": 75,
+      "disk_write_mb": 22
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "combat",
+    "metric_values": {
+      "ari": 0.7673,
+      "asw_batch": 0.9123,
+      "asw_label": 0.613,
+      "cell_cycle_conservation": 0.7925,
+      "clisi": 0.9997,
+      "graph_connectivity": 0.9728,
+      "hvg_overlap": 0.6649,
+      "ilisi": 0.1644,
+      "isolated_label_asw": "NA",
+      "isolated_label_f1": "NA",
+      "kbet": 0.1691,
+      "nmi": 0.8306,
+      "pcr": 0.9998
+    },
+    "scaled_scores": {
+      "ari": 0.7674,
+      "asw_batch": 0.8129,
+      "asw_label": 0.2471,
+      "cell_cycle_conservation": 0.9148,
+      "clisi": 0.999,
+      "graph_connectivity": 0.9639,
+      "hvg_overlap": 0.0573,
+      "ilisi": 0.3299,
+      "isolated_label_asw": 0,
+      "isolated_label_f1": 0,
+      "kbet": 0.1631,
+      "nmi": 0.8303,
+      "pcr": 0.9998
+    },
+    "mean_score": 0.545,
+    "resources": {
+      "submit": "2025-01-20 15:02:22",
+      "exit_code": 0,
+      "duration_sec": 307,
+      "cpu_pct": 146.3,
+      "peak_memory_mb": 8909,
+      "disk_read_mb": 68,
+      "disk_write_mb": 178
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "embed_cell_types",
+    "metric_values": {
+      "ari": 1,
+      "asw_batch": 0.9573,
+      "asw_label": "NA",
+      "cell_cycle_conservation": 0.81,
+      "clisi": 1,
+      "graph_connectivity": 1,
+      "hvg_overlap": "NA",
+      "ilisi": 0.4322,
+      "isolated_label_asw": "NA",
+      "isolated_label_f1": "NA",
+      "kbet": 0.9477,
+      "nmi": 1,
+      "pcr": 0.5897
+    },
+    "scaled_scores": {
+      "ari": 1,
+      "asw_batch": 0.9926,
+      "asw_label": 0,
+      "cell_cycle_conservation": 0.9365,
+      "clisi": 1,
+      "graph_connectivity": 1,
+      "hvg_overlap": 0,
+      "ilisi": 0.8934,
+      "isolated_label_asw": 0,
+      "isolated_label_f1": 0,
+      "kbet": 0.9966,
+      "nmi": 1,
+      "pcr": 0.5897
+    },
+    "mean_score": 0.6468,
+    "resources": {
+      "submit": "2025-01-20 15:02:23",
+      "exit_code": 0,
+      "duration_sec": 12.7,
+      "cpu_pct": 73.8,
+      "peak_memory_mb": 2868,
+      "disk_read_mb": 30,
+      "disk_write_mb": 5
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "embed_cell_types_jittered",
+    "metric_values": {
+      "ari": 1,
+      "asw_batch": 0.9591,
+      "asw_label": 0.9897,
+      "cell_cycle_conservation": 0.8102,
+      "clisi": 1,
+      "graph_connectivity": 1,
+      "hvg_overlap": "NA",
+      "ilisi": 0.431,
+      "isolated_label_asw": "NA",
+      "isolated_label_f1": "NA",
+      "kbet": 0.9509,
+      "nmi": 1,
+      "pcr": 0.5896
+    },
+    "scaled_scores": {
+      "ari": 1,
+      "asw_batch": 1,
+      "asw_label": 1,
+      "cell_cycle_conservation": 0.9368,
+      "clisi": 1,
+      "graph_connectivity": 1,
+      "hvg_overlap": 0,
+      "ilisi": 0.8908,
+      "isolated_label_asw": 0,
+      "isolated_label_f1": 0,
+      "kbet": 1,
+      "nmi": 1,
+      "pcr": 0.5896
+    },
+    "mean_score": 0.7244,
+    "resources": {
+      "submit": "2025-01-20 15:02:22",
+      "exit_code": 0,
+      "duration_sec": 10.5,
+      "cpu_pct": 107.4,
+      "peak_memory_mb": 5632,
+      "disk_read_mb": 30,
+      "disk_write_mb": 5
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "geneformer",
+    "metric_values": {
+      "ari": 0.0024,
+      "asw_batch": 0.4736,
+      "asw_label": 0.364,
+      "cell_cycle_conservation": 0.0527,
+      "clisi": "NA",
+      "graph_connectivity": 0.0109,
+      "hvg_overlap": "NA",
+      "ilisi": "NA",
+      "isolated_label_asw": "NA",
+      "isolated_label_f1": "NA",
+      "kbet": 0,
+      "nmi": 0.0315,
+      "pcr": 0
+    },
+    "scaled_scores": {
+      "ari": 0.0025,
+      "asw_batch": -0.9379,
+      "asw_label": -0.2506,
+      "cell_cycle_conservation": -0.0064,
+      "clisi": 0,
+      "graph_connectivity": -0.3102,
+      "hvg_overlap": 0,
+      "ilisi": 0,
+      "isolated_label_asw": 0,
+      "isolated_label_f1": 0,
+      "kbet": -0.0179,
+      "nmi": 0.03,
+      "pcr": 0
+    },
+    "mean_score": 0.0025,
+    "resources": {
+      "submit": "2025-01-20 15:02:22",
+      "exit_code": 0,
+      "duration_sec": 258,
+      "cpu_pct": 114.6,
+      "peak_memory_mb": 1048576,
+      "disk_read_mb": 4608,
+      "disk_write_mb": 4608
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "harmony",
+    "metric_values": {
+      "ari": 0.7643,
+      "asw_batch": 0.9012,
+      "asw_label": 0.647,
+      "cell_cycle_conservation": 0.8461,
+      "clisi": 1,
+      "graph_connectivity": 0.9763,
+      "hvg_overlap": "NA",
+      "ilisi": 0.3348,
+      "isolated_label_asw": "NA",
+      "isolated_label_f1": "NA",
+      "kbet": 0.426,
+      "nmi": 0.8295,
+      "pcr": 0.6773
+    },
+    "scaled_scores": {
+      "ari": 0.7643,
+      "asw_batch": 0.7689,
+      "asw_label": 0.3152,
+      "cell_cycle_conservation": 0.9816,
+      "clisi": 1,
+      "graph_connectivity": 0.9686,
+      "hvg_overlap": 0,
+      "ilisi": 0.6884,
+      "isolated_label_asw": 0,
+      "isolated_label_f1": 0,
+      "kbet": 0.4381,
+      "nmi": 0.8293,
+      "pcr": 0.6773
+    },
+    "mean_score": 0.5717,
+    "resources": {
+      "submit": "2025-01-20 15:02:22",
+      "exit_code": 0,
+      "duration_sec": 52.3,
+      "cpu_pct": 115.1,
+      "peak_memory_mb": 5120,
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+    "mean_score": 0,
+    "resources": {
+      "submit": "2025-01-20 15:11:10",
+      "exit_code": 1,
+      "duration_sec": 210,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/tabula_sapiens",
+    "method_id": "scprint",
+    "metric_values": {
+      "graph_connectivity": "NA",
+      "cell_cycle_conservation": "NA",
+      "ilisi": "NA",
+      "clisi": "NA",
+      "isolated_label_f1": "NA",
+      "pcr": "NA",
+      "isolated_label_asw": "NA",
+      "asw_label": "NA",
+      "kbet": "NA",
+      "asw_batch": "NA",
+      "ari": "NA",
+      "nmi": "NA",
+      "hvg_overlap": "NA"
+    },
+    "scaled_scores": {
+      "graph_connectivity": 0,
+      "cell_cycle_conservation": 0,
+      "ilisi": 0,
+      "clisi": 0,
+      "isolated_label_f1": 0,
+      "pcr": 0,
+      "isolated_label_asw": 0,
+      "asw_label": 0,
+      "kbet": 0,
+      "asw_batch": 0,
+      "ari": 0,
+      "nmi": 0,
+      "hvg_overlap": 0
+    },
+    "mean_score": 0,
+    "resources": {
+      "submit": "2025-01-20 15:40:10",
+      "exit_code": 1,
+      "duration_sec": 12581,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
+  }
+]
diff --git a/results/batch_integration/data/state.yaml b/results/batch_integration/data/state.yaml
new file mode 100644
index 00000000..abbb0fc1
--- /dev/null
+++ b/results/batch_integration/data/state.yaml
@@ -0,0 +1,9 @@
+id: process
+output_scores: !file results.json
+output_method_info: !file method_info.json
+output_metric_info: !file metric_info.json
+output_dataset_info: !file dataset_info.json
+output_task_info: !file task_info.json
+output_qc: !file quality_control.json
+output_metric_execution_info: !file metric_execution_info.json
+
diff --git a/results/batch_integration/data/task_info.json b/results/batch_integration/data/task_info.json
new file mode 100644
index 00000000..67a0fd8b
--- /dev/null
+++ b/results/batch_integration/data/task_info.json
@@ -0,0 +1,69 @@
+{
+  "task_id": "task_batch_integration",
+  "commit_sha": null,
+  "task_name": "Batch Integration",
+  "task_summary": "Remove unwanted batch effects from scRNA-seq data while retaining biologically meaningful variation.",
+  "task_description": "As single-cell technologies advance, single-cell datasets are growing both in size and complexity.\nEspecially in consortia such as the Human Cell Atlas, individual studies combine data from multiple labs, each sequencing multiple individuals possibly with different technologies.\nThis gives rise to complex batch effects in the data that must be computationally removed to perform a joint analysis.\nThese batch integration methods must remove the batch effect while not removing relevant biological information.\nCurrently, over 200 tools exist that aim to remove batch effects scRNA-seq datasets [@zappia2018exploring].\nThese methods balance the removal of batch effects with the conservation of nuanced biological information in different ways.\nThis abundance of tools has complicated batch integration method choice, leading to several benchmarks on this topic [@luecken2020benchmarking; @tran2020benchmark; @chazarragil2021flexible; @mereu2020benchmarking].\nYet, benchmarks use different metrics, method implementations and datasets. Here we build a living benchmarking task for batch integration methods with the vision of improving the consistency of method evaluation.\n\nIn this task we evaluate batch integration methods on their ability to remove batch effects in the data while conserving variation attributed to biological effects.\nAs input, methods require either normalised or unnormalised data with multiple batches and consistent cell type labels.\nThe batch integrated output can be a feature matrix, a low dimensional embedding and/or a neighbourhood graph.\nThe respective batch-integrated representation is then evaluated using sets of metrics that capture how well batch effects are removed and whether biological variance is conserved.\nWe have based this particular task on the latest, and most extensive benchmark of single-cell data integration methods.\n",
+  "repo": "https://github.com/openproblems-bio/task_batch_integration",
+  "issue_tracker": "https://github.com/openproblems-bio/task_batch_integration/issues",
+  "authors": [
+    {
+      "name": "Michaela Mueller",
+      "roles": ["maintainer", "author"],
+      "info": {
+        "github": "mumichae",
+        "orcid": "0000-0002-1401-1785"
+      }
+    },
+    {
+      "name": "Malte Luecken",
+      "roles": "author",
+      "info": {
+        "github": "LuckyMD",
+        "orcid": "0000-0001-7464-7921"
+      }
+    },
+    {
+      "name": "Daniel Strobl",
+      "roles": "author",
+      "info": {
+        "github": "danielStrobl",
+        "orcid": "0000-0002-5516-7057"
+      }
+    },
+    {
+      "name": "Robrecht Cannoodt",
+      "roles": "contributor",
+      "info": {
+        "github": "rcannood",
+        "orcid": "0000-0003-3641-729X"
+      }
+    },
+    {
+      "name": "Scott Gigante",
+      "roles": "contributor",
+      "info": {
+        "github": "scottgigante",
+        "orcid": "0000-0002-4544-2764"
+      }
+    },
+    {
+      "name": "Kai Waldrant",
+      "roles": "contributor",
+      "info": {
+        "github": "KaiWaldrant",
+        "orcid": "0009-0003-8555-1361"
+      }
+    },
+    {
+      "name": "Nartin Kim",
+      "roles": "contributor",
+      "info": {
+        "github": "martinkim0",
+        "orcid": "0009-0003-8555-1361"
+      }
+    }
+  ],
+  "version": "build_main",
+  "license": "MIT"
+}
diff --git a/results/batch_integration_graph/index.qmd b/results/batch_integration/index.qmd
similarity index 76%
rename from results/batch_integration_graph/index.qmd
rename to results/batch_integration/index.qmd
index 3d9e86e8..ddcbc03b 100644
--- a/results/batch_integration_graph/index.qmd
+++ b/results/batch_integration/index.qmd
@@ -1,6 +1,6 @@
 ---
-title: "Batch integration graph"
-subtitle: "Removing batch effects while preserving biological variation (graph output)"
+title: "Batch integration"
+subtitle: "Removing batch effects while preserving biological variation"
 image: thumbnail.svg
 page-layout: full
 css: ../_include/task_template.css
@@ -15,8 +15,9 @@ toc: false
 
 ```{r}
 #| include: false
-params <- list(data_dir = "results/batch_integration_graph/data")
+params <- list(data_dir = "results/batch_integration/data")
 params <- list(data_dir = "./data")
 ```
 
 {{< include ../_include/_task_template.qmd >}}
+
diff --git a/results/batch_integration_embed/thumbnail.svg b/results/batch_integration/thumbnail.svg
similarity index 100%
rename from results/batch_integration_embed/thumbnail.svg
rename to results/batch_integration/thumbnail.svg
diff --git a/results/batch_integration_embed/data/dataset_info.json b/results/batch_integration_embed/data/dataset_info.json
deleted file mode 100644
index 20699867..00000000
--- a/results/batch_integration_embed/data/dataset_info.json
+++ /dev/null
@@ -1,38 +0,0 @@
-[
-    {
-        "dataset_name": "Immune (by batch)",
-        "image": "openproblems",
-        "data_url": "https://ndownloader.figshare.com/files/36086786",
-        "data_reference": "luecken2022benchmarking",
-        "dataset_summary": "Human immune cells from peripheral blood and bone marrow taken from 5 datasets comprising 10 batches across technologies (10X, Smart-seq2).",
-        "task_id": "batch_integration_embed",
-        "commit_sha": "9d1665076cf6215a31f89ed2be8be20a02502887",
-        "dataset_id": "immune_batch",
-        "source_dataset_id": "openproblems_v1/immune_cells",
-        "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/_batch_integration/_common/datasets/immune.py"
-    },
-    {
-        "dataset_name": "Lung (Viera Braga et al.)",
-        "image": "openproblems",
-        "data_url": "https://figshare.com/ndownloader/files/24539942",
-        "data_reference": "luecken2022benchmarking",
-        "dataset_summary": "Human lung scRNA-seq data from 3 datasets with 32,472 cells. From Vieira Braga et al. Technologies: 10X and Drop-seq.",
-        "task_id": "batch_integration_embed",
-        "commit_sha": "9d1665076cf6215a31f89ed2be8be20a02502887",
-        "dataset_id": "lung_batch",
-        "source_dataset_id": "openproblems_v1/tabula_muris_senis_droplet_lung",
-        "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/_batch_integration/_common/datasets/lung.py"
-    },
-    {
-        "dataset_name": "Pancreas (by batch)",
-        "image": "openproblems",
-        "data_url": "https://ndownloader.figshare.com/files/36086813",
-        "data_reference": "luecken2022benchmarking",
-        "dataset_summary": "Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq).",
-        "task_id": "batch_integration_embed",
-        "commit_sha": "9d1665076cf6215a31f89ed2be8be20a02502887",
-        "dataset_id": "pancreas_batch",
-        "source_dataset_id": "openproblems_v1/pancreas",
-        "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/_batch_integration/_common/datasets/pancreas.py"
-    }
-]
\ No newline at end of file
diff --git a/results/batch_integration_embed/data/method_info.json b/results/batch_integration_embed/data/method_info.json
deleted file mode 100644
index 344519a0..00000000
--- a/results/batch_integration_embed/data/method_info.json
+++ /dev/null
@@ -1,602 +0,0 @@
-[
-  {
-    "method_name": "Random Integration by Batch",
-    "method_summary": "Feature values, embedding coordinates, and graph connectivity are all randomly permuted within each batch label",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
-    "is_baseline": true,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "batch_random_integration",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/_common/methods/baseline.py"
-  },
-  {
-    "method_name": "Random Embedding by Celltype",
-    "method_summary": "Cells are embedded as a one-hot encoding of celltype labels",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
-    "is_baseline": true,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "celltype_random_embedding",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/methods/baseline.py"
-  },
-  {
-    "method_name": "Random Embedding by Celltype (with jitter)",
-    "method_summary": "Cells are embedded as a one-hot encoding of celltype labels, with a small amount of random noise added to the embedding",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
-    "is_baseline": true,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "celltype_random_embedding_jitter",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/methods/baseline.py"
-  },
-  {
-    "method_name": "Random Graph by Celltype",
-    "method_summary": "Cells are embedded as a one-hot encoding of celltype labels. A graph is then built on this embedding",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
-    "is_baseline": true,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "celltype_random_graph",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/baseline.py"
-  },
-  {
-    "method_name": "Random Integration by Celltype",
-    "method_summary": "Feature values, embedding coordinates, and graph connectivity are all randomly permuted within each celltype label",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
-    "is_baseline": true,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "celltype_random_integration",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/_common/methods/baseline.py"
-  },
-  {
-    "method_name": "Combat (full/scaled)",
-    "method_summary": "ComBat uses an Empirical Bayes (EB) approach to correct for batch effects. It estimates batch-specific parameters by pooling information across genes in each batch and shrinks the estimates towards the overall mean of the batch effect estimates across all genes. These parameters are then used to adjust the data for batch effects, leading to more accurate and reproducible results.",
-    "paper_name": "Adjusting batch effects in microarray expression data using empirical Bayes methods",
-    "paper_reference": "hansen2012removing",
-    "paper_year": 2007,
-    "code_url": "https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.combat.html/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "combat_full_scaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/combat.py"
-  },
-  {
-    "method_name": "Combat (full/unscaled)",
-    "method_summary": "ComBat uses an Empirical Bayes (EB) approach to correct for batch effects. It estimates batch-specific parameters by pooling information across genes in each batch and shrinks the estimates towards the overall mean of the batch effect estimates across all genes. These parameters are then used to adjust the data for batch effects, leading to more accurate and reproducible results.",
-    "paper_name": "Adjusting batch effects in microarray expression data using empirical Bayes methods",
-    "paper_reference": "hansen2012removing",
-    "paper_year": 2007,
-    "code_url": "https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.combat.html/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "combat_full_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/combat.py"
-  },
-  {
-    "method_name": "Combat (hvg/scaled)",
-    "method_summary": "ComBat uses an Empirical Bayes (EB) approach to correct for batch effects. It estimates batch-specific parameters by pooling information across genes in each batch and shrinks the estimates towards the overall mean of the batch effect estimates across all genes. These parameters are then used to adjust the data for batch effects, leading to more accurate and reproducible results.",
-    "paper_name": "Adjusting batch effects in microarray expression data using empirical Bayes methods",
-    "paper_reference": "hansen2012removing",
-    "paper_year": 2007,
-    "code_url": "https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.combat.html/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "combat_hvg_scaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/combat.py"
-  },
-  {
-    "method_name": "Combat (hvg/unscaled)",
-    "method_summary": "ComBat uses an Empirical Bayes (EB) approach to correct for batch effects. It estimates batch-specific parameters by pooling information across genes in each batch and shrinks the estimates towards the overall mean of the batch effect estimates across all genes. These parameters are then used to adjust the data for batch effects, leading to more accurate and reproducible results.",
-    "paper_name": "Adjusting batch effects in microarray expression data using empirical Bayes methods",
-    "paper_reference": "hansen2012removing",
-    "paper_year": 2007,
-    "code_url": "https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.combat.html/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "combat_hvg_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/combat.py"
-  },
-  {
-    "method_name": "FastMNN embed (full/scaled)",
-    "method_summary": "fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step.",
-    "paper_name": "A description of the theory behind the fastMNN algorithm",
-    "paper_reference": "lun2019fastmnn",
-    "paper_year": 2019,
-    "code_url": "https://doi.org/doi:10.18129/B9.bioc.batchelor/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "fastmnn_embed_full_scaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py"
-  },
-  {
-    "method_name": "FastMNN embed (full/unscaled)",
-    "method_summary": "fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step.",
-    "paper_name": "A description of the theory behind the fastMNN algorithm",
-    "paper_reference": "lun2019fastmnn",
-    "paper_year": 2019,
-    "code_url": "https://doi.org/doi:10.18129/B9.bioc.batchelor/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "fastmnn_embed_full_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py"
-  },
-  {
-    "method_name": "FastMNN embed (hvg/scaled)",
-    "method_summary": "fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step.",
-    "paper_name": "A description of the theory behind the fastMNN algorithm",
-    "paper_reference": "lun2019fastmnn",
-    "paper_year": 2019,
-    "code_url": "https://doi.org/doi:10.18129/B9.bioc.batchelor/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "fastmnn_embed_hvg_scaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py"
-  },
-  {
-    "method_name": "FastMNN embed (hvg/unscaled)",
-    "method_summary": "fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step.",
-    "paper_name": "A description of the theory behind the fastMNN algorithm",
-    "paper_reference": "lun2019fastmnn",
-    "paper_year": 2019,
-    "code_url": "https://doi.org/doi:10.18129/B9.bioc.batchelor/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
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-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "fastmnn_embed_hvg_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py"
-  },
-  {
-    "method_name": "Harmony (full/scaled)",
-    "method_summary": "Harmony is a method that uses PCA to group the cells into multi-dataset clusters, and then computes cluster-specific linear correction factors. Each cell is then corrected by its cell-specific linear factor using the cluster-weighted average. The method keeps iterating these four steps until cell clusters are stable.",
-    "paper_name": "Fast, sensitive and accurate integration of single-cell data with Harmony",
-    "paper_reference": "korsunsky2019fast",
-    "paper_year": 2019,
-    "code_url": "https://github.com/lilab-bcb/harmony-pytorch/tree/v1.0.0/openproblems/tasks",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/harmony.py"
-  },
-  {
-    "method_name": "Harmony (full/unscaled)",
-    "method_summary": "Harmony is a method that uses PCA to group the cells into multi-dataset clusters, and then computes cluster-specific linear correction factors. Each cell is then corrected by its cell-specific linear factor using the cluster-weighted average. The method keeps iterating these four steps until cell clusters are stable.",
-    "paper_name": "Fast, sensitive and accurate integration of single-cell data with Harmony",
-    "paper_reference": "korsunsky2019fast",
-    "paper_year": 2019,
-    "code_url": "https://github.com/lilab-bcb/harmony-pytorch/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/harmony.py"
-  },
-  {
-    "method_name": "Harmony (hvg/scaled)",
-    "method_summary": "Harmony is a method that uses PCA to group the cells into multi-dataset clusters, and then computes cluster-specific linear correction factors. Each cell is then corrected by its cell-specific linear factor using the cluster-weighted average. The method keeps iterating these four steps until cell clusters are stable.",
-    "paper_name": "Fast, sensitive and accurate integration of single-cell data with Harmony",
-    "paper_reference": "korsunsky2019fast",
-    "paper_year": 2019,
-    "code_url": "https://github.com/lilab-bcb/harmony-pytorch/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
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-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/harmony.py"
-  },
-  {
-    "method_name": "Harmony (hvg/unscaled)",
-    "method_summary": "Harmony is a method that uses PCA to group the cells into multi-dataset clusters, and then computes cluster-specific linear correction factors. Each cell is then corrected by its cell-specific linear factor using the cluster-weighted average. The method keeps iterating these four steps until cell clusters are stable.",
-    "paper_name": "Fast, sensitive and accurate integration of single-cell data with Harmony",
-    "paper_reference": "korsunsky2019fast",
-    "paper_year": 2019,
-    "code_url": "https://github.com/lilab-bcb/harmony-pytorch/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/harmony.py"
-  },
-  {
-    "method_name": "Liger (full/unscaled)",
-    "method_summary": "LIGER or linked inference of genomic experimental relationships uses iNMF deriving and implementing a novel coordinate descent algorithm to efficiently do the factorization. Joint clustering is performed and factor loadings are normalised.",
-    "paper_name": "Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity",
-    "paper_reference": "welch2019single",
-    "paper_year": 2019,
-    "code_url": "https://github.com/welch-lab/liger/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
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-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/liger.py"
-  },
-  {
-    "method_name": "Liger (hvg/unscaled)",
-    "method_summary": "LIGER or linked inference of genomic experimental relationships uses iNMF deriving and implementing a novel coordinate descent algorithm to efficiently do the factorization. Joint clustering is performed and factor loadings are normalised.",
-    "paper_name": "Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity",
-    "paper_reference": "welch2019single",
-    "paper_year": 2019,
-    "code_url": "https://github.com/welch-lab/liger/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
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-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/liger.py"
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-  {
-    "method_name": "MNN (full/scaled)",
-    "method_summary": "MNN first detects mutual nearest neighbours in two of the batches and infers a projection of the second onto the first batch. After that, additional batches are added iteratively.",
-    "paper_name": "Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors",
-    "paper_reference": "haghverdi2018batch",
-    "paper_year": 2018,
-    "code_url": "https://github.com/chriscainx/mnnpy/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/mnn.py"
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-  {
-    "method_name": "MNN (full/unscaled)",
-    "method_summary": "MNN first detects mutual nearest neighbours in two of the batches and infers a projection of the second onto the first batch. After that, additional batches are added iteratively.",
-    "paper_name": "Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors",
-    "paper_reference": "haghverdi2018batch",
-    "paper_year": 2018,
-    "code_url": "https://github.com/chriscainx/mnnpy/tree/v1.0.0/openproblems/tasks",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/mnn.py"
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-  {
-    "method_name": "MNN (hvg/scaled)",
-    "method_summary": "MNN first detects mutual nearest neighbours in two of the batches and infers a projection of the second onto the first batch. After that, additional batches are added iteratively.",
-    "paper_name": "Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors",
-    "paper_reference": "haghverdi2018batch",
-    "paper_year": 2018,
-    "code_url": "https://github.com/chriscainx/mnnpy/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/mnn.py"
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-  {
-    "method_name": "MNN (hvg/unscaled)",
-    "method_summary": "MNN first detects mutual nearest neighbours in two of the batches and infers a projection of the second onto the first batch. After that, additional batches are added iteratively.",
-    "paper_name": "Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors",
-    "paper_reference": "haghverdi2018batch",
-    "paper_year": 2018,
-    "code_url": "https://github.com/chriscainx/mnnpy/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/mnn.py"
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-  {
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-    "method_summary": "Cells are embedded by PCA on the unintegrated data. A graph is built on this PCA embedding.",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/_common/methods/baseline.py"
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-  {
-    "method_name": "No Integration by Batch",
-    "method_summary": "Cells are embedded by computing PCA independently on each batch",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
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-    "code_version": "v1.0.0",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/methods/baseline.py"
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-  {
-    "method_name": "Random Integration",
-    "method_summary": "Feature values, embedding coordinates, and graph connectivity are all randomly permuted",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/_common/methods/baseline.py"
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-  {
-    "method_name": "SCALEX (full)",
-    "method_summary": "SCALEX is a method for integrating heterogeneous single-cell data online using a VAE framework. Its generalised encoder disentangles batch-related components from batch-invariant biological components, which are then projected into a common cell-embedding space.",
-    "paper_name": "Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space",
-    "paper_reference": "xiong2021online",
-    "paper_year": 2022,
-    "code_url": "https://github.com/jsxlei/SCALEX/tree/v1.0.0/openproblems/tasks",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/methods/scalex.py"
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-  {
-    "method_name": "SCALEX (hvg)",
-    "method_summary": "SCALEX is a method for integrating heterogeneous single-cell data online using a VAE framework. Its generalised encoder disentangles batch-related components from batch-invariant biological components, which are then projected into a common cell-embedding space.",
-    "paper_name": "Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space",
-    "paper_reference": "xiong2021online",
-    "paper_year": 2022,
-    "code_url": "https://github.com/jsxlei/SCALEX/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-pytorch",
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-    "code_version": "v1.0.0",
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-    "method_id": "scalex_hvg",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/methods/scalex.py"
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-  {
-    "method_name": "Scanorama (full/scaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "code_version": "v1.0.0",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py"
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-  {
-    "method_name": "Scanorama (full/unscaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py"
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-  {
-    "method_name": "Scanorama (hvg/scaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py"
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-  {
-    "method_name": "Scanorama (hvg/unscaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py"
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-  {
-    "method_name": "Scanorama gene output (full/scaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py"
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-  {
-    "method_name": "Scanorama gene output (full/unscaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "scanorama_feature_full_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py"
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-  {
-    "method_name": "Scanorama gene output (hvg/scaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "scanorama_feature_hvg_scaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py"
-  },
-  {
-    "method_name": "Scanorama gene output (hvg/unscaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "scanorama_feature_hvg_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py"
-  },
-  {
-    "method_name": "scANVI (full/unscaled)",
-    "method_summary": "ScanVI is an extension of scVI but instead using a Bayesian semi-supervised approach for more principled cell annotation.",
-    "paper_name": "Probabilistic harmonization and annotation of single\u2010cell transcriptomics data with deep generative models",
-    "paper_reference": "xu2021probabilistic",
-    "paper_year": 2021,
-    "code_url": "https://github.com/YosefLab/scvi-tools/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "scanvi_full_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanvi.py"
-  },
-  {
-    "method_name": "scANVI (hvg/unscaled)",
-    "method_summary": "ScanVI is an extension of scVI but instead using a Bayesian semi-supervised approach for more principled cell annotation.",
-    "paper_name": "Probabilistic harmonization and annotation of single\u2010cell transcriptomics data with deep generative models",
-    "paper_reference": "xu2021probabilistic",
-    "paper_year": 2021,
-    "code_url": "https://github.com/YosefLab/scvi-tools/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "scanvi_hvg_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanvi.py"
-  },
-  {
-    "method_name": "scVI (full/unscaled)",
-    "method_summary": "scVI combines a variational autoencoder with a hierarchical Bayesian model.",
-    "paper_name": "Deep generative modeling for single-cell transcriptomics",
-    "paper_reference": "lopez2018deep",
-    "paper_year": 2018,
-    "code_url": "https://github.com/YosefLab/scvi-tools/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "scvi_full_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scvi.py"
-  },
-  {
-    "method_name": "scVI (hvg/unscaled)",
-    "method_summary": "scVI combines a variational autoencoder with a hierarchical Bayesian model.",
-    "paper_name": "Deep generative modeling for single-cell transcriptomics",
-    "paper_reference": "lopez2018deep",
-    "paper_year": 2018,
-    "code_url": "https://github.com/YosefLab/scvi-tools/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "scvi_hvg_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scvi.py"
-  }
-]
\ No newline at end of file
diff --git a/results/batch_integration_embed/data/metric_info.json b/results/batch_integration_embed/data/metric_info.json
deleted file mode 100644
index ce8874ae..00000000
--- a/results/batch_integration_embed/data/metric_info.json
+++ /dev/null
@@ -1,122 +0,0 @@
-[
-  {
-    "metric_name": "ARI",
-    "metric_summary": "ARI (Adjusted Rand Index) compares the overlap of two clusterings. It considers both correct clustering overlaps while also counting correct disagreements between two clustering.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "ee7836251c4c6c371471e95eb7aa6a3e9f133b43",
-    "metric_id": "ari",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/ari.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "Cell Cycle Score",
-    "metric_summary": "The cell-cycle conservation score evaluates how well the cell-cycle effect can be captured before and after integration.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "metric_id": "cc_score",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/cc_score.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "Graph connectivity",
-    "metric_summary": "The graph connectivity metric assesses whether the kNN graph representation, G, of the integrated data connects all cells with the same cell identity label.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "ee7836251c4c6c371471e95eb7aa6a3e9f133b43",
-    "metric_id": "graph_connectivity",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/graph_connectivity.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "Isolated label F1",
-    "metric_summary": "Isolated cell labels are identified as the labels present in the least number of batches in the integration task. The score evaluates how well these isolated labels separate from other cell identities based on clustering.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "ee7836251c4c6c371471e95eb7aa6a3e9f133b43",
-    "metric_id": "isolated_labels_f1",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/iso_label_f1.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "Isolated label Silhouette",
-    "metric_summary": "This score evaluates the compactness for the label(s) that is(are) shared by fewest batches. It indicates how well rare cell types can be preserved after integration.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "metric_id": "isolated_labels_sil",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/iso_label_sil.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "kBET",
-    "metric_summary": "kBET determines whether the label composition of a k nearest neighborhood of a cell is similar to the expected (global) label composition. The test is repeated for a random subset of cells, and the results are summarized as a rejection rate over all tested neighborhoods.",
-    "paper_reference": "bttner2018test",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "metric_id": "kBET",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/kBET.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "NMI",
-    "metric_summary": "NMI compares the overlap of two clusterings. We used NMI to compare the cell-type labels with Louvain clusters computed on the integrated dataset.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "ee7836251c4c6c371471e95eb7aa6a3e9f133b43",
-    "metric_id": "nmi",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/nmi.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "PC Regression",
-    "metric_summary": "This compares the explained variance by batch before and after integration. It returns a score between 0 and 1 (scaled=True) with 0 if the variance contribution hasn\u2019t changed. The larger the score, the more different the variance contributions are before and after integration.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "metric_id": "pcr",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/pcr.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "Silhouette",
-    "metric_summary": "The absolute silhouette with is computed on cell identity labels, measuring their compactness.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "metric_id": "silhouette",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/silhouette.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "Batch ASW",
-    "metric_summary": "The absolute silhouette width is computed over batch labels per cell. As 0 then indicates that batches are well mixed and any deviation from 0 indicates a batch effect, we use the 1-abs(ASW) to map the score to the scale [0;1].",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_embed",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "metric_id": "silhouette_batch",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/sil_batch.py",
-    "code_version": "v1.0.0"
-  }
-]
\ No newline at end of file
diff --git a/results/batch_integration_embed/data/quality_control.json b/results/batch_integration_embed/data/quality_control.json
deleted file mode 100644
index c4407ccf..00000000
--- a/results/batch_integration_embed/data/quality_control.json
+++ /dev/null
@@ -1,8372 +0,0 @@
-[
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Task info", 
-        "name": "Pct 'task_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_id' should be defined\n  Task id: batch_integration_embed\n  Field: task_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Task info", 
-        "name": "Pct 'commit_sha' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'commit_sha' should be defined\n  Task id: batch_integration_embed\n  Field: commit_sha\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Task info", 
-        "name": "Pct 'task_name' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_name' should be defined\n  Task id: batch_integration_embed\n  Field: task_name\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Task info", 
-        "name": "Pct 'task_summary' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_summary' should be defined\n  Task id: batch_integration_embed\n  Field: task_summary\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Task info", 
-        "name": "Pct 'task_description' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_description' should be defined\n  Task id: batch_integration_embed\n  Field: task_description\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Method info", 
-        "name": "Pct 'task_id' missing", 
-        "value": 0.0, 
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-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'task_id' should be defined\n  Task id: batch_integration_embed\n  Field: task_id\n"
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-        "message": "Method metadata field 'commit_sha' should be defined\n  Task id: batch_integration_embed\n  Field: commit_sha\n"
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-        "task_id": "batch_integration_embed", 
-        "category": "Method info", 
-        "name": "Pct 'method_id' missing", 
-        "value": 0.0, 
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-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'method_id' should be defined\n  Task id: batch_integration_embed\n  Field: method_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Method info", 
-        "name": "Pct 'method_name' missing", 
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-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'method_name' should be defined\n  Task id: batch_integration_embed\n  Field: method_name\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Method info", 
-        "name": "Pct 'method_summary' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'method_summary' should be defined\n  Task id: batch_integration_embed\n  Field: method_summary\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Method info", 
-        "name": "Pct 'paper_reference' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'paper_reference' should be defined\n  Task id: batch_integration_embed\n  Field: paper_reference\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Method info", 
-        "name": "Pct 'is_baseline' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'is_baseline' should be defined\n  Task id: batch_integration_embed\n  Field: is_baseline\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Metric info", 
-        "name": "Pct 'task_id' missing", 
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-        "severity": 0, 
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-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'task_id' should be defined\n  Task id: batch_integration_embed\n  Field: task_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Metric info", 
-        "name": "Pct 'commit_sha' missing", 
-        "value": 0.0, 
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-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'commit_sha' should be defined\n  Task id: batch_integration_embed\n  Field: commit_sha\n"
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-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Metric info", 
-        "name": "Pct 'metric_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'metric_id' should be defined\n  Task id: batch_integration_embed\n  Field: metric_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Metric info", 
-        "name": "Pct 'metric_name' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'metric_name' should be defined\n  Task id: batch_integration_embed\n  Field: metric_name\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Metric info", 
-        "name": "Pct 'metric_summary' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'metric_summary' should be defined\n  Task id: batch_integration_embed\n  Field: metric_summary\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Metric info", 
-        "name": "Pct 'paper_reference' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'paper_reference' should be defined\n  Task id: batch_integration_embed\n  Field: paper_reference\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Metric info", 
-        "name": "Pct 'maximize' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'maximize' should be defined\n  Task id: batch_integration_embed\n  Field: maximize\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Dataset info", 
-        "name": "Pct 'task_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'task_id' should be defined\n  Task id: batch_integration_embed\n  Field: task_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Dataset info", 
-        "name": "Pct 'commit_sha' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'commit_sha' should be defined\n  Task id: batch_integration_embed\n  Field: commit_sha\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Dataset info", 
-        "name": "Pct 'dataset_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'dataset_id' should be defined\n  Task id: batch_integration_embed\n  Field: dataset_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Dataset info", 
-        "name": "Pct 'dataset_name' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'dataset_name' should be defined\n  Task id: batch_integration_embed\n  Field: dataset_name\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Dataset info", 
-        "name": "Pct 'dataset_summary' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'dataset_summary' should be defined\n  Task id: batch_integration_embed\n  Field: dataset_summary\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Dataset info", 
-        "name": "Pct 'data_reference' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'data_reference' should be defined\n  Task id: batch_integration_embed\n  Field: data_reference\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw data", 
-        "name": "Number of results", 
-        "value": 120, 
-        "severity": 0, 
-        "severity_value": -0.5263157894736836, 
-        "code": "len(results) == len(method_info) * len(metric_info) * len(dataset_info)", 
-        "message": "Number of results should be equal to #methods × #metrics × #datasets.\n  Task id: batch_integration_embed\n  Number of results: 120\n  Number of methods: 38\n  Number of metrics: 10\n  Number of datasets: 3\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Metric 'ari' %missing", 
-        "value": -0.05263157894736836, 
-        "severity": 0, 
-        "severity_value": -0.5263157894736836, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  Metric id: ari\n  Percentage missing: -5%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Metric 'cc_score' %missing", 
-        "value": -0.05263157894736836, 
-        "severity": 0, 
-        "severity_value": -0.5263157894736836, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  Metric id: cc_score\n  Percentage missing: -5%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Metric 'graph_connectivity' %missing", 
-        "value": -0.05263157894736836, 
-        "severity": 0, 
-        "severity_value": -0.5263157894736836, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  Metric id: graph_connectivity\n  Percentage missing: -5%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Metric 'isolated_labels_f1' %missing", 
-        "value": -0.05263157894736836, 
-        "severity": 0, 
-        "severity_value": -0.5263157894736836, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  Metric id: isolated_labels_f1\n  Percentage missing: -5%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Metric 'isolated_labels_sil' %missing", 
-        "value": -0.05263157894736836, 
-        "severity": 0, 
-        "severity_value": -0.5263157894736836, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  Metric id: isolated_labels_sil\n  Percentage missing: -5%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Metric 'kBET' %missing", 
-        "value": -0.05263157894736836, 
-        "severity": 0, 
-        "severity_value": -0.5263157894736836, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  Metric id: kBET\n  Percentage missing: -5%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Metric 'nmi' %missing", 
-        "value": -0.05263157894736836, 
-        "severity": 0, 
-        "severity_value": -0.5263157894736836, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  Metric id: nmi\n  Percentage missing: -5%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Metric 'pcr' %missing", 
-        "value": -0.05263157894736836, 
-        "severity": 0, 
-        "severity_value": -0.5263157894736836, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  Metric id: pcr\n  Percentage missing: -5%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Metric 'silhouette' %missing", 
-        "value": -0.05263157894736836, 
-        "severity": 0, 
-        "severity_value": -0.5263157894736836, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  Metric id: silhouette\n  Percentage missing: -5%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Metric 'silhouette_batch' %missing", 
-        "value": -0.05263157894736836, 
-        "severity": 0, 
-        "severity_value": -0.5263157894736836, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  Metric id: silhouette_batch\n  Percentage missing: -5%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'batch_random_integration' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: batch_random_integration\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'celltype_random_embedding' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: celltype_random_embedding\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'celltype_random_integration' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: celltype_random_integration\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'combat_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: combat_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'combat_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: combat_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'combat_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: combat_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'combat_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: combat_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_embed_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: fastmnn_embed_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_embed_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: fastmnn_embed_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_embed_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: fastmnn_embed_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_embed_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: fastmnn_embed_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'harmony_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: harmony_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'harmony_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: harmony_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'harmony_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: harmony_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'harmony_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: harmony_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'liger_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: liger_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'liger_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: liger_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'mnn_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: mnn_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'mnn_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: mnn_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'mnn_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: mnn_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'mnn_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: mnn_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'no_integration' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: no_integration\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'no_integration_batch' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: no_integration_batch\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'random_integration' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: random_integration\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'scalex_full' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: scalex_full\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'scalex_hvg' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: scalex_hvg\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_embed_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: scanorama_embed_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_embed_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: scanorama_embed_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_embed_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: scanorama_embed_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_embed_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: scanorama_embed_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_feature_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: scanorama_feature_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_feature_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: scanorama_feature_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_feature_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: scanorama_feature_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_feature_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: scanorama_feature_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'scanvi_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: scanvi_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'scanvi_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: scanvi_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'scvi_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: scvi_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Method 'scvi_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  method id: scvi_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Dataset 'immune_batch' %missing", 
-        "value": -0.05263157894736836, 
-        "severity": 0, 
-        "severity_value": -0.5263157894736836, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  dataset id: immune_batch\n  Percentage missing: -5%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Dataset 'lung_batch' %missing", 
-        "value": -0.05263157894736836, 
-        "severity": 0, 
-        "severity_value": -0.5263157894736836, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  dataset id: lung_batch\n  Percentage missing: -5%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Raw results", 
-        "name": "Dataset 'pancreas_batch' %missing", 
-        "value": -0.05263157894736836, 
-        "severity": 0, 
-        "severity_value": -0.5263157894736836, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_embed\n  dataset id: pancreas_batch\n  Percentage missing: -5%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration ari", 
-        "value": 0.011494336403337996, 
-        "severity": 0, 
-        "severity_value": -0.011494336403337996, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: ari\n  Worst score: 0.011494336403337996%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration ari", 
-        "value": 0.13357601064452126, 
-        "severity": 0, 
-        "severity_value": 0.06678800532226063, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: ari\n  Best score: 0.13357601064452126%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_embedding ari", 
-        "value": 0.9993024106016116, 
-        "severity": 0, 
-        "severity_value": -0.9993024106016116, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_embedding performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: ari\n  Worst score: 0.9993024106016116%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_embedding ari", 
-        "value": 0.9998041343109118, 
-        "severity": 0, 
-        "severity_value": 0.4999020671554559, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_embedding performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: ari\n  Best score: 0.9998041343109118%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration ari", 
-        "value": 0.21731285514824517, 
-        "severity": 0, 
-        "severity_value": -0.21731285514824517, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: ari\n  Worst score: 0.21731285514824517%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration ari", 
-        "value": 0.35574995394099357, 
-        "severity": 0, 
-        "severity_value": 0.17787497697049678, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: ari\n  Best score: 0.35574995394099357%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled ari", 
-        "value": 0.32517514676217535, 
-        "severity": 0, 
-        "severity_value": -0.32517514676217535, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: ari\n  Worst score: 0.32517514676217535%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled ari", 
-        "value": 0.6985707382479861, 
-        "severity": 0, 
-        "severity_value": 0.34928536912399305, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: ari\n  Best score: 0.6985707382479861%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled ari", 
-        "value": 0.5627512627334263, 
-        "severity": 0, 
-        "severity_value": -0.5627512627334263, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: ari\n  Worst score: 0.5627512627334263%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled ari", 
-        "value": 0.9483162315872511, 
-        "severity": 0, 
-        "severity_value": 0.47415811579362555, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: ari\n  Best score: 0.9483162315872511%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled ari", 
-        "value": 0.4659206414661972, 
-        "severity": 0, 
-        "severity_value": -0.4659206414661972, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: ari\n  Worst score: 0.4659206414661972%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled ari", 
-        "value": 0.947582158839779, 
-        "severity": 0, 
-        "severity_value": 0.4737910794198895, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: ari\n  Best score: 0.947582158839779%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled ari", 
-        "value": 0.5504261799330875, 
-        "severity": 0, 
-        "severity_value": -0.5504261799330875, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.5504261799330875%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled ari", 
-        "value": 0.9443875992717398, 
-        "severity": 0, 
-        "severity_value": 0.4721937996358699, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: ari\n  Best score: 0.9443875992717398%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_scaled ari", 
-        "value": 0.4905620199258996, 
-        "severity": 0, 
-        "severity_value": -0.4905620199258996, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: ari\n  Worst score: 0.4905620199258996%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_scaled ari", 
-        "value": 0.8828873407813143, 
-        "severity": 0, 
-        "severity_value": 0.44144367039065713, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: ari\n  Best score: 0.8828873407813143%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_unscaled ari", 
-        "value": 0.4917821023975586, 
-        "severity": 0, 
-        "severity_value": -0.4917821023975586, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: ari\n  Worst score: 0.4917821023975586%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_unscaled ari", 
-        "value": 0.8838893162637474, 
-        "severity": 0, 
-        "severity_value": 0.4419446581318737, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: ari\n  Best score: 0.8838893162637474%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_scaled ari", 
-        "value": 0.5761986247759455, 
-        "severity": 0, 
-        "severity_value": -0.5761986247759455, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: ari\n  Worst score: 0.5761986247759455%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_scaled ari", 
-        "value": 0.8426620326223145, 
-        "severity": 0, 
-        "severity_value": 0.42133101631115727, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: ari\n  Best score: 0.8426620326223145%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_unscaled ari", 
-        "value": 0.6260051920927225, 
-        "severity": 0, 
-        "severity_value": -0.6260051920927225, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.6260051920927225%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_unscaled ari", 
-        "value": 0.9268209144368924, 
-        "severity": 0, 
-        "severity_value": 0.4634104572184462, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: ari\n  Best score: 0.9268209144368924%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_scaled ari", 
-        "value": 0.39992323277423597, 
-        "severity": 0, 
-        "severity_value": -0.39992323277423597, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: ari\n  Worst score: 0.39992323277423597%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_scaled ari", 
-        "value": 0.8246170327480875, 
-        "severity": 0, 
-        "severity_value": 0.41230851637404375, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: ari\n  Best score: 0.8246170327480875%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_unscaled ari", 
-        "value": 0.460902785634702, 
-        "severity": 0, 
-        "severity_value": -0.460902785634702, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: ari\n  Worst score: 0.460902785634702%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_unscaled ari", 
-        "value": 0.9041754572680524, 
-        "severity": 0, 
-        "severity_value": 0.4520877286340262, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: ari\n  Best score: 0.9041754572680524%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_scaled ari", 
-        "value": 0.48029963834937156, 
-        "severity": 0, 
-        "severity_value": -0.48029963834937156, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: ari\n  Worst score: 0.48029963834937156%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_scaled ari", 
-        "value": 0.9063463566540584, 
-        "severity": 0, 
-        "severity_value": 0.4531731783270292, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: ari\n  Best score: 0.9063463566540584%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_unscaled ari", 
-        "value": 0.5208805629431544, 
-        "severity": 0, 
-        "severity_value": -0.5208805629431544, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.5208805629431544%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_unscaled ari", 
-        "value": 0.9447712000339032, 
-        "severity": 0, 
-        "severity_value": 0.4723856000169516, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: ari\n  Best score: 0.9447712000339032%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_full_unscaled ari", 
-        "value": 0.051913403148777695, 
-        "severity": 0, 
-        "severity_value": -0.051913403148777695, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: ari\n  Worst score: 0.051913403148777695%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_full_unscaled ari", 
-        "value": 0.5063995123854445, 
-        "severity": 0, 
-        "severity_value": 0.2531997561927222, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: ari\n  Best score: 0.5063995123854445%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_hvg_unscaled ari", 
-        "value": 0.08753625529406613, 
-        "severity": 0, 
-        "severity_value": -0.08753625529406613, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.08753625529406613%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_hvg_unscaled ari", 
-        "value": 0.6950947346866869, 
-        "severity": 0, 
-        "severity_value": 0.34754736734334346, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: ari\n  Best score: 0.6950947346866869%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled ari", 
-        "value": 0.41729488173607077, 
-        "severity": 0, 
-        "severity_value": -0.41729488173607077, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: ari\n  Worst score: 0.41729488173607077%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled ari", 
-        "value": 0.7167488544657117, 
-        "severity": 0, 
-        "severity_value": 0.35837442723285584, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: ari\n  Best score: 0.7167488544657117%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled ari", 
-        "value": 0.4921460704960394, 
-        "severity": 0, 
-        "severity_value": -0.4921460704960394, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: ari\n  Worst score: 0.4921460704960394%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled ari", 
-        "value": 0.8419826848709305, 
-        "severity": 0, 
-        "severity_value": 0.42099134243546527, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: ari\n  Best score: 0.8419826848709305%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled ari", 
-        "value": 0.5155250267434874, 
-        "severity": 0, 
-        "severity_value": -0.5155250267434874, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: ari\n  Worst score: 0.5155250267434874%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled ari", 
-        "value": 0.9447831560425927, 
-        "severity": 0, 
-        "severity_value": 0.47239157802129633, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: ari\n  Best score: 0.9447831560425927%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled ari", 
-        "value": 0.4768195194352643, 
-        "severity": 0, 
-        "severity_value": -0.4768195194352643, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.4768195194352643%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled ari", 
-        "value": 0.8474577575576652, 
-        "severity": 0, 
-        "severity_value": 0.4237288787788326, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: ari\n  Best score: 0.8474577575576652%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration ari", 
-        "value": 0.2190327973060119, 
-        "severity": 0, 
-        "severity_value": -0.2190327973060119, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: ari\n  Worst score: 0.2190327973060119%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration ari", 
-        "value": 0.39348262383026966, 
-        "severity": 0, 
-        "severity_value": 0.19674131191513483, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: ari\n  Best score: 0.39348262383026966%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration_batch ari", 
-        "value": 0.14356452533963954, 
-        "severity": 0, 
-        "severity_value": -0.14356452533963954, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: ari\n  Worst score: 0.14356452533963954%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration_batch ari", 
-        "value": 0.39888624043876997, 
-        "severity": 0, 
-        "severity_value": 0.19944312021938498, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: ari\n  Best score: 0.39888624043876997%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration ari", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: ari\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score random_integration ari", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: ari\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full ari", 
-        "value": 0.5812235063369019, 
-        "severity": 0, 
-        "severity_value": -0.5812235063369019, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: ari\n  Worst score: 0.5812235063369019%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full ari", 
-        "value": 0.9185490107147578, 
-        "severity": 0, 
-        "severity_value": 0.4592745053573789, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: ari\n  Best score: 0.9185490107147578%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg ari", 
-        "value": 0.5925092264954488, 
-        "severity": 0, 
-        "severity_value": -0.5925092264954488, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: ari\n  Worst score: 0.5925092264954488%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg ari", 
-        "value": 0.9423505910859357, 
-        "severity": 0, 
-        "severity_value": 0.47117529554296783, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: ari\n  Best score: 0.9423505910859357%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_scaled ari", 
-        "value": 0.46039414604620427, 
-        "severity": 0, 
-        "severity_value": -0.46039414604620427, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: ari\n  Worst score: 0.46039414604620427%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_scaled ari", 
-        "value": 0.9126205147986652, 
-        "severity": 0, 
-        "severity_value": 0.4563102573993326, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: ari\n  Best score: 0.9126205147986652%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_unscaled ari", 
-        "value": 0.47757527991334286, 
-        "severity": 0, 
-        "severity_value": -0.47757527991334286, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: ari\n  Worst score: 0.47757527991334286%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_unscaled ari", 
-        "value": 0.6010909901281, 
-        "severity": 0, 
-        "severity_value": 0.30054549506405, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: ari\n  Best score: 0.6010909901281%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_scaled ari", 
-        "value": 0.6035452930312094, 
-        "severity": 0, 
-        "severity_value": -0.6035452930312094, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: ari\n  Worst score: 0.6035452930312094%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_scaled ari", 
-        "value": 0.9502685195323741, 
-        "severity": 0, 
-        "severity_value": 0.47513425976618706, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: ari\n  Best score: 0.9502685195323741%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_unscaled ari", 
-        "value": 0.5013391045859101, 
-        "severity": 0, 
-        "severity_value": -0.5013391045859101, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.5013391045859101%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_unscaled ari", 
-        "value": 0.9556291894359813, 
-        "severity": 0, 
-        "severity_value": 0.47781459471799065, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: ari\n  Best score: 0.9556291894359813%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled ari", 
-        "value": 0.4345768281450408, 
-        "severity": 0, 
-        "severity_value": -0.4345768281450408, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: ari\n  Worst score: 0.4345768281450408%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled ari", 
-        "value": 0.6978031176981246, 
-        "severity": 0, 
-        "severity_value": 0.3489015588490623, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: ari\n  Best score: 0.6978031176981246%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled ari", 
-        "value": 0.4385989126612417, 
-        "severity": 0, 
-        "severity_value": -0.4385989126612417, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: ari\n  Worst score: 0.4385989126612417%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled ari", 
-        "value": 0.6466310939687293, 
-        "severity": 0, 
-        "severity_value": 0.32331554698436465, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: ari\n  Best score: 0.6466310939687293%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled ari", 
-        "value": 0.43294957649297644, 
-        "severity": 0, 
-        "severity_value": -0.43294957649297644, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: ari\n  Worst score: 0.43294957649297644%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled ari", 
-        "value": 0.8913864715792952, 
-        "severity": 0, 
-        "severity_value": 0.4456932357896476, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: ari\n  Best score: 0.8913864715792952%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled ari", 
-        "value": 0.4808643661574091, 
-        "severity": 0, 
-        "severity_value": -0.4808643661574091, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.4808643661574091%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled ari", 
-        "value": 0.7222193291450928, 
-        "severity": 0, 
-        "severity_value": 0.3611096645725464, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: ari\n  Best score: 0.7222193291450928%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_full_unscaled ari", 
-        "value": 0.7045843356448059, 
-        "severity": 0, 
-        "severity_value": -0.7045843356448059, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: ari\n  Worst score: 0.7045843356448059%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_full_unscaled ari", 
-        "value": 0.9485439116014002, 
-        "severity": 0, 
-        "severity_value": 0.4742719558007001, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: ari\n  Best score: 0.9485439116014002%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_hvg_unscaled ari", 
-        "value": 0.7648011431243055, 
-        "severity": 0, 
-        "severity_value": -0.7648011431243055, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.7648011431243055%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_hvg_unscaled ari", 
-        "value": 0.9536215153321025, 
-        "severity": 0, 
-        "severity_value": 0.47681075766605124, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: ari\n  Best score: 0.9536215153321025%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_full_unscaled ari", 
-        "value": 0.5891639699345362, 
-        "severity": 0, 
-        "severity_value": -0.5891639699345362, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: ari\n  Worst score: 0.5891639699345362%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_full_unscaled ari", 
-        "value": 0.9447727361286155, 
-        "severity": 0, 
-        "severity_value": 0.4723863680643077, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: ari\n  Best score: 0.9447727361286155%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_hvg_unscaled ari", 
-        "value": 0.574540101892011, 
-        "severity": 0, 
-        "severity_value": -0.574540101892011, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.574540101892011%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_hvg_unscaled ari", 
-        "value": 0.9449675823457939, 
-        "severity": 0, 
-        "severity_value": 0.47248379117289696, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: ari\n  Best score: 0.9449675823457939%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration cc_score", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: cc_score\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration cc_score", 
-        "value": 0.015410497930393091, 
-        "severity": 0, 
-        "severity_value": 0.0077052489651965456, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: cc_score\n  Best score: 0.015410497930393091%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_embedding cc_score", 
-        "value": 0.42743722688144686, 
-        "severity": 0, 
-        "severity_value": -0.42743722688144686, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_embedding performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: cc_score\n  Worst score: 0.42743722688144686%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_embedding cc_score", 
-        "value": 0.7586431788574569, 
-        "severity": 0, 
-        "severity_value": 0.37932158942872846, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_embedding performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: cc_score\n  Best score: 0.7586431788574569%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration cc_score", 
-        "value": 0.13693848440984396, 
-        "severity": 0, 
-        "severity_value": -0.13693848440984396, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: cc_score\n  Worst score: 0.13693848440984396%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration cc_score", 
-        "value": 0.5145181005836906, 
-        "severity": 0, 
-        "severity_value": 0.2572590502918453, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: cc_score\n  Best score: 0.5145181005836906%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled cc_score", 
-        "value": 0.45351197305029517, 
-        "severity": 0, 
-        "severity_value": -0.45351197305029517, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: cc_score\n  Worst score: 0.45351197305029517%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled cc_score", 
-        "value": 0.7927558603002866, 
-        "severity": 0, 
-        "severity_value": 0.3963779301501433, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: cc_score\n  Best score: 0.7927558603002866%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled cc_score", 
-        "value": 0.5782486141994184, 
-        "severity": 0, 
-        "severity_value": -0.5782486141994184, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: cc_score\n  Worst score: 0.5782486141994184%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled cc_score", 
-        "value": 0.8015039644443673, 
-        "severity": 0, 
-        "severity_value": 0.40075198222218367, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: cc_score\n  Best score: 0.8015039644443673%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled cc_score", 
-        "value": 0.5808270735888832, 
-        "severity": 0, 
-        "severity_value": -0.5808270735888832, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: cc_score\n  Worst score: 0.5808270735888832%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled cc_score", 
-        "value": 0.7709726265027038, 
-        "severity": 0, 
-        "severity_value": 0.3854863132513519, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: cc_score\n  Best score: 0.7709726265027038%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled cc_score", 
-        "value": 0.7578992607839113, 
-        "severity": 0, 
-        "severity_value": -0.7578992607839113, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: cc_score\n  Worst score: 0.7578992607839113%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled cc_score", 
-        "value": 0.86324912752735, 
-        "severity": 0, 
-        "severity_value": 0.431624563763675, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: cc_score\n  Best score: 0.86324912752735%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_scaled cc_score", 
-        "value": 0.33350571854520966, 
-        "severity": 0, 
-        "severity_value": -0.33350571854520966, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: cc_score\n  Worst score: 0.33350571854520966%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_scaled cc_score", 
-        "value": 0.9076223244248717, 
-        "severity": 0, 
-        "severity_value": 0.45381116221243584, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: cc_score\n  Best score: 0.9076223244248717%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_unscaled cc_score", 
-        "value": 0.33350874810695463, 
-        "severity": 0, 
-        "severity_value": -0.33350874810695463, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: cc_score\n  Worst score: 0.33350874810695463%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_unscaled cc_score", 
-        "value": 0.9075841477247601, 
-        "severity": 0, 
-        "severity_value": 0.4537920738623801, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: cc_score\n  Best score: 0.9075841477247601%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_scaled cc_score", 
-        "value": 0.6430644669249569, 
-        "severity": 0, 
-        "severity_value": -0.6430644669249569, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: cc_score\n  Worst score: 0.6430644669249569%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_scaled cc_score", 
-        "value": 0.8138074230488564, 
-        "severity": 0, 
-        "severity_value": 0.4069037115244282, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: cc_score\n  Best score: 0.8138074230488564%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_unscaled cc_score", 
-        "value": 0.6430637665381265, 
-        "severity": 0, 
-        "severity_value": -0.6430637665381265, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: cc_score\n  Worst score: 0.6430637665381265%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_unscaled cc_score", 
-        "value": 0.8141735960586463, 
-        "severity": 0, 
-        "severity_value": 0.4070867980293231, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: cc_score\n  Best score: 0.8141735960586463%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_scaled cc_score", 
-        "value": 0.46515490110169794, 
-        "severity": 0, 
-        "severity_value": -0.46515490110169794, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: cc_score\n  Worst score: 0.46515490110169794%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_scaled cc_score", 
-        "value": 0.8847991294343871, 
-        "severity": 0, 
-        "severity_value": 0.44239956471719355, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: cc_score\n  Best score: 0.8847991294343871%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_unscaled cc_score", 
-        "value": 0.7039928341748172, 
-        "severity": 0, 
-        "severity_value": -0.7039928341748172, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: cc_score\n  Worst score: 0.7039928341748172%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_unscaled cc_score", 
-        "value": 0.747969779177605, 
-        "severity": 0, 
-        "severity_value": 0.3739848895888025, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: cc_score\n  Best score: 0.747969779177605%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_scaled cc_score", 
-        "value": 0.6816414031522995, 
-        "severity": 0, 
-        "severity_value": -0.6816414031522995, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: cc_score\n  Worst score: 0.6816414031522995%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_scaled cc_score", 
-        "value": 0.7951632804497045, 
-        "severity": 0, 
-        "severity_value": 0.39758164022485226, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: cc_score\n  Best score: 0.7951632804497045%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_unscaled cc_score", 
-        "value": 0.6861830254308678, 
-        "severity": 0, 
-        "severity_value": -0.6861830254308678, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: cc_score\n  Worst score: 0.6861830254308678%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_unscaled cc_score", 
-        "value": 0.8667789109591572, 
-        "severity": 0, 
-        "severity_value": 0.4333894554795786, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: cc_score\n  Best score: 0.8667789109591572%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_full_unscaled cc_score", 
-        "value": 0.12858257177529248, 
-        "severity": 0, 
-        "severity_value": -0.12858257177529248, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: cc_score\n  Worst score: 0.12858257177529248%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_full_unscaled cc_score", 
-        "value": 0.6158076037222415, 
-        "severity": 0, 
-        "severity_value": 0.30790380186112076, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: cc_score\n  Best score: 0.6158076037222415%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_hvg_unscaled cc_score", 
-        "value": 0.17003892224418718, 
-        "severity": 0, 
-        "severity_value": -0.17003892224418718, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: cc_score\n  Worst score: 0.17003892224418718%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_hvg_unscaled cc_score", 
-        "value": 0.589089054749381, 
-        "severity": 0, 
-        "severity_value": 0.2945445273746905, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: cc_score\n  Best score: 0.589089054749381%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled cc_score", 
-        "value": 0.6572452376899656, 
-        "severity": 0, 
-        "severity_value": -0.6572452376899656, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: cc_score\n  Worst score: 0.6572452376899656%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled cc_score", 
-        "value": 0.868181330076587, 
-        "severity": 0, 
-        "severity_value": 0.4340906650382935, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: cc_score\n  Best score: 0.868181330076587%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled cc_score", 
-        "value": 0.5427614508847851, 
-        "severity": 0, 
-        "severity_value": -0.5427614508847851, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: cc_score\n  Worst score: 0.5427614508847851%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled cc_score", 
-        "value": 0.8132876410630354, 
-        "severity": 0, 
-        "severity_value": 0.4066438205315177, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: cc_score\n  Best score: 0.8132876410630354%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled cc_score", 
-        "value": 0.5474282290769672, 
-        "severity": 0, 
-        "severity_value": -0.5474282290769672, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: cc_score\n  Worst score: 0.5474282290769672%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled cc_score", 
-        "value": 0.8990953481773007, 
-        "severity": 0, 
-        "severity_value": 0.4495476740886504, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: cc_score\n  Best score: 0.8990953481773007%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled cc_score", 
-        "value": 0.7479330924208999, 
-        "severity": 0, 
-        "severity_value": -0.7479330924208999, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: cc_score\n  Worst score: 0.7479330924208999%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled cc_score", 
-        "value": 0.8443763904443078, 
-        "severity": 0, 
-        "severity_value": 0.4221881952221539, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: cc_score\n  Best score: 0.8443763904443078%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration cc_score", 
-        "value": 0.6596616693640952, 
-        "severity": 0, 
-        "severity_value": -0.6596616693640952, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: cc_score\n  Worst score: 0.6596616693640952%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration cc_score", 
-        "value": 0.7475082485741233, 
-        "severity": 0, 
-        "severity_value": 0.3737541242870617, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: cc_score\n  Best score: 0.7475082485741233%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration_batch cc_score", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: cc_score\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration_batch cc_score", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: cc_score\n  Best score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration cc_score", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: cc_score\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score random_integration cc_score", 
-        "value": 0.03437830347413983, 
-        "severity": 0, 
-        "severity_value": 0.017189151737069915, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: cc_score\n  Best score: 0.03437830347413983%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full cc_score", 
-        "value": 0.6219741898532707, 
-        "severity": 0, 
-        "severity_value": -0.6219741898532707, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: cc_score\n  Worst score: 0.6219741898532707%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full cc_score", 
-        "value": 0.7718287625147161, 
-        "severity": 0, 
-        "severity_value": 0.38591438125735805, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: cc_score\n  Best score: 0.7718287625147161%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg cc_score", 
-        "value": 0.7237366480227464, 
-        "severity": 0, 
-        "severity_value": -0.7237366480227464, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: cc_score\n  Worst score: 0.7237366480227464%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg cc_score", 
-        "value": 0.8585231202579875, 
-        "severity": 0, 
-        "severity_value": 0.42926156012899375, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: cc_score\n  Best score: 0.8585231202579875%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_scaled cc_score", 
-        "value": 0.031769122291166414, 
-        "severity": 0, 
-        "severity_value": -0.031769122291166414, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: cc_score\n  Worst score: 0.031769122291166414%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_scaled cc_score", 
-        "value": 0.44993190857778564, 
-        "severity": 0, 
-        "severity_value": 0.22496595428889282, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: cc_score\n  Best score: 0.44993190857778564%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_unscaled cc_score", 
-        "value": 0.049106027622488994, 
-        "severity": 0, 
-        "severity_value": -0.049106027622488994, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: cc_score\n  Worst score: 0.049106027622488994%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_unscaled cc_score", 
-        "value": 0.4070768995167817, 
-        "severity": 0, 
-        "severity_value": 0.20353844975839086, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: cc_score\n  Best score: 0.4070768995167817%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_scaled cc_score", 
-        "value": 0.04009433262717032, 
-        "severity": 0, 
-        "severity_value": -0.04009433262717032, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: cc_score\n  Worst score: 0.04009433262717032%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_scaled cc_score", 
-        "value": 0.4464612444083037, 
-        "severity": 0, 
-        "severity_value": 0.22323062220415185, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: cc_score\n  Best score: 0.4464612444083037%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_unscaled cc_score", 
-        "value": 0.04923412466128331, 
-        "severity": 0, 
-        "severity_value": -0.04923412466128331, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: cc_score\n  Worst score: 0.04923412466128331%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_unscaled cc_score", 
-        "value": 0.42751544796751534, 
-        "severity": 0, 
-        "severity_value": 0.21375772398375767, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: cc_score\n  Best score: 0.42751544796751534%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled cc_score", 
-        "value": 0.013985661729761244, 
-        "severity": 0, 
-        "severity_value": -0.013985661729761244, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: cc_score\n  Worst score: 0.013985661729761244%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled cc_score", 
-        "value": 0.45727576100510364, 
-        "severity": 0, 
-        "severity_value": 0.22863788050255182, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: cc_score\n  Best score: 0.45727576100510364%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled cc_score", 
-        "value": 0.07759460928654092, 
-        "severity": 0, 
-        "severity_value": -0.07759460928654092, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: cc_score\n  Worst score: 0.07759460928654092%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled cc_score", 
-        "value": 0.3907764268817029, 
-        "severity": 0, 
-        "severity_value": 0.19538821344085144, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: cc_score\n  Best score: 0.3907764268817029%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled cc_score", 
-        "value": 0.04777662940729437, 
-        "severity": 0, 
-        "severity_value": -0.04777662940729437, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: cc_score\n  Worst score: 0.04777662940729437%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled cc_score", 
-        "value": 0.42772718968308826, 
-        "severity": 0, 
-        "severity_value": 0.21386359484154413, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: cc_score\n  Best score: 0.42772718968308826%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled cc_score", 
-        "value": 0.05836457000783451, 
-        "severity": 0, 
-        "severity_value": -0.05836457000783451, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: cc_score\n  Worst score: 0.05836457000783451%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled cc_score", 
-        "value": 0.38157222055888756, 
-        "severity": 0, 
-        "severity_value": 0.19078611027944378, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: cc_score\n  Best score: 0.38157222055888756%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_full_unscaled cc_score", 
-        "value": 0.5305841685283994, 
-        "severity": 0, 
-        "severity_value": -0.5305841685283994, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: cc_score\n  Worst score: 0.5305841685283994%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_full_unscaled cc_score", 
-        "value": 0.7381577520015773, 
-        "severity": 0, 
-        "severity_value": 0.36907887600078865, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: cc_score\n  Best score: 0.7381577520015773%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_hvg_unscaled cc_score", 
-        "value": 0.43651584801050797, 
-        "severity": 0, 
-        "severity_value": -0.43651584801050797, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: cc_score\n  Worst score: 0.43651584801050797%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_hvg_unscaled cc_score", 
-        "value": 0.6823805309847765, 
-        "severity": 0, 
-        "severity_value": 0.34119026549238823, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: cc_score\n  Best score: 0.6823805309847765%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_full_unscaled cc_score", 
-        "value": 0.48892451280493676, 
-        "severity": 0, 
-        "severity_value": -0.48892451280493676, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: cc_score\n  Worst score: 0.48892451280493676%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_full_unscaled cc_score", 
-        "value": 0.6451972570095569, 
-        "severity": 0, 
-        "severity_value": 0.32259862850477844, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: cc_score\n  Best score: 0.6451972570095569%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_hvg_unscaled cc_score", 
-        "value": 0.3866396431176305, 
-        "severity": 0, 
-        "severity_value": -0.3866396431176305, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: cc_score\n  Worst score: 0.3866396431176305%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_hvg_unscaled cc_score", 
-        "value": 0.6773862280282567, 
-        "severity": 0, 
-        "severity_value": 0.3386931140141283, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: cc_score\n  Best score: 0.6773862280282567%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration graph_connectivity", 
-        "value": 0.0709887403148445, 
-        "severity": 0, 
-        "severity_value": -0.0709887403148445, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: graph_connectivity\n  Worst score: 0.0709887403148445%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration graph_connectivity", 
-        "value": 0.4409945314118057, 
-        "severity": 0, 
-        "severity_value": 0.22049726570590286, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: graph_connectivity\n  Best score: 0.4409945314118057%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_embedding graph_connectivity", 
-        "value": 0.9874301831743929, 
-        "severity": 0, 
-        "severity_value": -0.9874301831743929, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_embedding performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: graph_connectivity\n  Worst score: 0.9874301831743929%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_embedding graph_connectivity", 
-        "value": 0.9992195686147407, 
-        "severity": 0, 
-        "severity_value": 0.49960978430737035, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_embedding performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: graph_connectivity\n  Best score: 0.9992195686147407%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration graph_connectivity", 
-        "value": 0.7781254152725039, 
-        "severity": 0, 
-        "severity_value": -0.7781254152725039, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: graph_connectivity\n  Worst score: 0.7781254152725039%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration graph_connectivity", 
-        "value": 0.7946595387513147, 
-        "severity": 0, 
-        "severity_value": 0.39732976937565734, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: graph_connectivity\n  Best score: 0.7946595387513147%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled graph_connectivity", 
-        "value": 0.9316229403504982, 
-        "severity": 0, 
-        "severity_value": -0.9316229403504982, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9316229403504982%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled graph_connectivity", 
-        "value": 0.9757028358287112, 
-        "severity": 0, 
-        "severity_value": 0.4878514179143556, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9757028358287112%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled graph_connectivity", 
-        "value": 0.9347809715030857, 
-        "severity": 0, 
-        "severity_value": -0.9347809715030857, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9347809715030857%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled graph_connectivity", 
-        "value": 0.9921048949674441, 
-        "severity": 0, 
-        "severity_value": 0.49605244748372207, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9921048949674441%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled graph_connectivity", 
-        "value": 0.9299765117798222, 
-        "severity": 0, 
-        "severity_value": -0.9299765117798222, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9299765117798222%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled graph_connectivity", 
-        "value": 0.9940569120091668, 
-        "severity": 0, 
-        "severity_value": 0.4970284560045834, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9940569120091668%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled graph_connectivity", 
-        "value": 0.9479723600844172, 
-        "severity": 0, 
-        "severity_value": -0.9479723600844172, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9479723600844172%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled graph_connectivity", 
-        "value": 0.9937807665809447, 
-        "severity": 0, 
-        "severity_value": 0.49689038329047236, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9937807665809447%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_scaled graph_connectivity", 
-        "value": 0.9463355210213086, 
-        "severity": 0, 
-        "severity_value": -0.9463355210213086, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9463355210213086%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_scaled graph_connectivity", 
-        "value": 0.9725068715285502, 
-        "severity": 0, 
-        "severity_value": 0.4862534357642751, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9725068715285502%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_unscaled graph_connectivity", 
-        "value": 0.9463969609934103, 
-        "severity": 0, 
-        "severity_value": -0.9463969609934103, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9463969609934103%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_unscaled graph_connectivity", 
-        "value": 0.9701810583553837, 
-        "severity": 0, 
-        "severity_value": 0.48509052917769185, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9701810583553837%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_scaled graph_connectivity", 
-        "value": 0.9392933967734716, 
-        "severity": 0, 
-        "severity_value": -0.9392933967734716, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9392933967734716%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_scaled graph_connectivity", 
-        "value": 0.9727611559999784, 
-        "severity": 0, 
-        "severity_value": 0.4863805779999892, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9727611559999784%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_unscaled graph_connectivity", 
-        "value": 0.94306657648511, 
-        "severity": 0, 
-        "severity_value": -0.94306657648511, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.94306657648511%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_unscaled graph_connectivity", 
-        "value": 0.9738996742261249, 
-        "severity": 0, 
-        "severity_value": 0.48694983711306244, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9738996742261249%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_scaled graph_connectivity", 
-        "value": 0.890356779753995, 
-        "severity": 0, 
-        "severity_value": -0.890356779753995, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.890356779753995%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_scaled graph_connectivity", 
-        "value": 0.9843498585675281, 
-        "severity": 0, 
-        "severity_value": 0.49217492928376405, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9843498585675281%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_unscaled graph_connectivity", 
-        "value": 0.9143451613560141, 
-        "severity": 0, 
-        "severity_value": -0.9143451613560141, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9143451613560141%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_unscaled graph_connectivity", 
-        "value": 0.9887924002011376, 
-        "severity": 0, 
-        "severity_value": 0.4943962001005688, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9887924002011376%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_scaled graph_connectivity", 
-        "value": 0.8989041321922118, 
-        "severity": 0, 
-        "severity_value": -0.8989041321922118, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.8989041321922118%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_scaled graph_connectivity", 
-        "value": 0.9819335777993654, 
-        "severity": 0, 
-        "severity_value": 0.4909667888996827, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9819335777993654%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_unscaled graph_connectivity", 
-        "value": 0.9303594623930925, 
-        "severity": 0, 
-        "severity_value": -0.9303594623930925, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9303594623930925%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_unscaled graph_connectivity", 
-        "value": 0.9922242148089679, 
-        "severity": 0, 
-        "severity_value": 0.49611210740448397, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9922242148089679%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_full_unscaled graph_connectivity", 
-        "value": 0.4549034444819604, 
-        "severity": 0, 
-        "severity_value": -0.4549034444819604, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.4549034444819604%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_full_unscaled graph_connectivity", 
-        "value": 0.8745928563490009, 
-        "severity": 0, 
-        "severity_value": 0.43729642817450043, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.8745928563490009%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_hvg_unscaled graph_connectivity", 
-        "value": 0.3393739330126555, 
-        "severity": 0, 
-        "severity_value": -0.3393739330126555, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.3393739330126555%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_hvg_unscaled graph_connectivity", 
-        "value": 0.8675513374727145, 
-        "severity": 0, 
-        "severity_value": 0.43377566873635726, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.8675513374727145%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled graph_connectivity", 
-        "value": 0.9604951313673952, 
-        "severity": 0, 
-        "severity_value": -0.9604951313673952, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9604951313673952%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled graph_connectivity", 
-        "value": 0.9778960878644275, 
-        "severity": 0, 
-        "severity_value": 0.48894804393221375, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9778960878644275%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled graph_connectivity", 
-        "value": 0.9855719324977882, 
-        "severity": 0, 
-        "severity_value": -0.9855719324977882, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9855719324977882%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled graph_connectivity", 
-        "value": 0.9907306158934754, 
-        "severity": 0, 
-        "severity_value": 0.4953653079467377, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9907306158934754%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled graph_connectivity", 
-        "value": 0.972330036756183, 
-        "severity": 0, 
-        "severity_value": -0.972330036756183, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.972330036756183%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled graph_connectivity", 
-        "value": 0.9931589882392966, 
-        "severity": 0, 
-        "severity_value": 0.4965794941196483, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9931589882392966%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled graph_connectivity", 
-        "value": 0.9789686620034518, 
-        "severity": 0, 
-        "severity_value": -0.9789686620034518, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9789686620034518%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled graph_connectivity", 
-        "value": 0.9941693733642543, 
-        "severity": 0, 
-        "severity_value": 0.49708468668212713, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9941693733642543%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration graph_connectivity", 
-        "value": 0.7781254152725039, 
-        "severity": 0, 
-        "severity_value": -0.7781254152725039, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: graph_connectivity\n  Worst score: 0.7781254152725039%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration graph_connectivity", 
-        "value": 0.7946595387513147, 
-        "severity": 0, 
-        "severity_value": 0.39732976937565734, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: graph_connectivity\n  Best score: 0.7946595387513147%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration_batch graph_connectivity", 
-        "value": 0.5623920208637583, 
-        "severity": 0, 
-        "severity_value": -0.5623920208637583, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: graph_connectivity\n  Worst score: 0.5623920208637583%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration_batch graph_connectivity", 
-        "value": 0.767444783120535, 
-        "severity": 0, 
-        "severity_value": 0.3837223915602675, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: graph_connectivity\n  Best score: 0.767444783120535%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration graph_connectivity", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: graph_connectivity\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score random_integration graph_connectivity", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: graph_connectivity\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full graph_connectivity", 
-        "value": 0.9558226468993593, 
-        "severity": 0, 
-        "severity_value": -0.9558226468993593, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: graph_connectivity\n  Worst score: 0.9558226468993593%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full graph_connectivity", 
-        "value": 0.9877167492847663, 
-        "severity": 0, 
-        "severity_value": 0.49385837464238314, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: graph_connectivity\n  Best score: 0.9877167492847663%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg graph_connectivity", 
-        "value": 0.9605882160998583, 
-        "severity": 0, 
-        "severity_value": -0.9605882160998583, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: graph_connectivity\n  Worst score: 0.9605882160998583%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg graph_connectivity", 
-        "value": 0.9923563079925779, 
-        "severity": 0, 
-        "severity_value": 0.49617815399628895, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: graph_connectivity\n  Best score: 0.9923563079925779%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_scaled graph_connectivity", 
-        "value": 0.9477421003488894, 
-        "severity": 0, 
-        "severity_value": -0.9477421003488894, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9477421003488894%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_scaled graph_connectivity", 
-        "value": 0.9929299416984546, 
-        "severity": 0, 
-        "severity_value": 0.4964649708492273, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9929299416984546%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_unscaled graph_connectivity", 
-        "value": 0.8175464983209577, 
-        "severity": 0, 
-        "severity_value": -0.8175464983209577, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.8175464983209577%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_unscaled graph_connectivity", 
-        "value": 0.9871836050720559, 
-        "severity": 0, 
-        "severity_value": 0.49359180253602797, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9871836050720559%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_scaled graph_connectivity", 
-        "value": 0.9481820628614359, 
-        "severity": 0, 
-        "severity_value": -0.9481820628614359, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9481820628614359%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_scaled graph_connectivity", 
-        "value": 0.9944252027107076, 
-        "severity": 0, 
-        "severity_value": 0.4972126013553538, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9944252027107076%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_unscaled graph_connectivity", 
-        "value": 0.8990187154631197, 
-        "severity": 0, 
-        "severity_value": -0.8990187154631197, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.8990187154631197%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_unscaled graph_connectivity", 
-        "value": 0.9941372531692639, 
-        "severity": 0, 
-        "severity_value": 0.49706862658463197, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9941372531692639%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled graph_connectivity", 
-        "value": 0.8698588341654329, 
-        "severity": 0, 
-        "severity_value": -0.8698588341654329, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.8698588341654329%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled graph_connectivity", 
-        "value": 0.9795438505400909, 
-        "severity": 0, 
-        "severity_value": 0.4897719252700454, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9795438505400909%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled graph_connectivity", 
-        "value": 0.7706506027014892, 
-        "severity": 0, 
-        "severity_value": -0.7706506027014892, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.7706506027014892%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled graph_connectivity", 
-        "value": 0.9820867949940939, 
-        "severity": 0, 
-        "severity_value": 0.49104339749704695, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9820867949940939%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled graph_connectivity", 
-        "value": 0.8463527101617427, 
-        "severity": 0, 
-        "severity_value": -0.8463527101617427, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.8463527101617427%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled graph_connectivity", 
-        "value": 0.988221846625954, 
-        "severity": 0, 
-        "severity_value": 0.494110923312977, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.988221846625954%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled graph_connectivity", 
-        "value": 0.8505149586490243, 
-        "severity": 0, 
-        "severity_value": -0.8505149586490243, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.8505149586490243%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled graph_connectivity", 
-        "value": 0.9843101121091732, 
-        "severity": 0, 
-        "severity_value": 0.4921550560545866, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9843101121091732%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_full_unscaled graph_connectivity", 
-        "value": 0.9824246296640182, 
-        "severity": 0, 
-        "severity_value": -0.9824246296640182, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9824246296640182%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_full_unscaled graph_connectivity", 
-        "value": 0.9951221344998192, 
-        "severity": 0, 
-        "severity_value": 0.4975610672499096, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9951221344998192%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_hvg_unscaled graph_connectivity", 
-        "value": 0.9785740616668632, 
-        "severity": 0, 
-        "severity_value": -0.9785740616668632, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9785740616668632%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_hvg_unscaled graph_connectivity", 
-        "value": 0.9947623341651014, 
-        "severity": 0, 
-        "severity_value": 0.4973811670825507, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9947623341651014%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_full_unscaled graph_connectivity", 
-        "value": 0.9788321728088012, 
-        "severity": 0, 
-        "severity_value": -0.9788321728088012, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9788321728088012%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_full_unscaled graph_connectivity", 
-        "value": 0.9958759026199181, 
-        "severity": 0, 
-        "severity_value": 0.49793795130995905, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9958759026199181%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_hvg_unscaled graph_connectivity", 
-        "value": 0.9808775713679067, 
-        "severity": 0, 
-        "severity_value": -0.9808775713679067, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9808775713679067%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_hvg_unscaled graph_connectivity", 
-        "value": 0.9947100646380034, 
-        "severity": 0, 
-        "severity_value": 0.4973550323190017, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9947100646380034%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration isolated_labels_f1", 
-        "value": 0.03350972243509662, 
-        "severity": 0, 
-        "severity_value": -0.03350972243509662, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: isolated_labels_f1\n  Worst score: 0.03350972243509662%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration isolated_labels_f1", 
-        "value": 0.10446963868812992, 
-        "severity": 0, 
-        "severity_value": 0.05223481934406496, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: isolated_labels_f1\n  Best score: 0.10446963868812992%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_embedding isolated_labels_f1", 
-        "value": 0.9915775810661778, 
-        "severity": 0, 
-        "severity_value": -0.9915775810661778, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_embedding performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: isolated_labels_f1\n  Worst score: 0.9915775810661778%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_embedding isolated_labels_f1", 
-        "value": 0.9975654425999245, 
-        "severity": 0, 
-        "severity_value": 0.49878272129996226, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_embedding performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: isolated_labels_f1\n  Best score: 0.9975654425999245%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration isolated_labels_f1", 
-        "value": 0.6949130898945103, 
-        "severity": 0, 
-        "severity_value": -0.6949130898945103, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: isolated_labels_f1\n  Worst score: 0.6949130898945103%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration isolated_labels_f1", 
-        "value": 0.7777045320204234, 
-        "severity": 0, 
-        "severity_value": 0.3888522660102117, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: isolated_labels_f1\n  Best score: 0.7777045320204234%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled isolated_labels_f1", 
-        "value": 0.22264899505565477, 
-        "severity": 0, 
-        "severity_value": -0.22264899505565477, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.22264899505565477%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled isolated_labels_f1", 
-        "value": 0.7331248440862427, 
-        "severity": 0, 
-        "severity_value": 0.36656242204312134, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.7331248440862427%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled isolated_labels_f1", 
-        "value": 0.7078275299517155, 
-        "severity": 0, 
-        "severity_value": -0.7078275299517155, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7078275299517155%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled isolated_labels_f1", 
-        "value": 0.9494404441546309, 
-        "severity": 0, 
-        "severity_value": 0.47472022207731546, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9494404441546309%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled isolated_labels_f1", 
-        "value": 0.7007901505830946, 
-        "severity": 0, 
-        "severity_value": -0.7007901505830946, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7007901505830946%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled isolated_labels_f1", 
-        "value": 0.9545968423259787, 
-        "severity": 0, 
-        "severity_value": 0.4772984211629894, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9545968423259787%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled isolated_labels_f1", 
-        "value": 0.7179827633640512, 
-        "severity": 0, 
-        "severity_value": -0.7179827633640512, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7179827633640512%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled isolated_labels_f1", 
-        "value": 0.9255591085428129, 
-        "severity": 0, 
-        "severity_value": 0.46277955427140643, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9255591085428129%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_scaled isolated_labels_f1", 
-        "value": 0.6320372664600578, 
-        "severity": 0, 
-        "severity_value": -0.6320372664600578, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.6320372664600578%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_scaled isolated_labels_f1", 
-        "value": 0.7418137411780066, 
-        "severity": 0, 
-        "severity_value": 0.3709068705890033, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.7418137411780066%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_unscaled isolated_labels_f1", 
-        "value": 0.633331624664782, 
-        "severity": 0, 
-        "severity_value": -0.633331624664782, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.633331624664782%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_unscaled isolated_labels_f1", 
-        "value": 0.773168988695132, 
-        "severity": 0, 
-        "severity_value": 0.386584494347566, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.773168988695132%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_scaled isolated_labels_f1", 
-        "value": 0.7237441883656265, 
-        "severity": 0, 
-        "severity_value": -0.7237441883656265, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7237441883656265%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_scaled isolated_labels_f1", 
-        "value": 0.8446914519418899, 
-        "severity": 0, 
-        "severity_value": 0.42234572597094494, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8446914519418899%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_unscaled isolated_labels_f1", 
-        "value": 0.7195801349603234, 
-        "severity": 0, 
-        "severity_value": -0.7195801349603234, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7195801349603234%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_unscaled isolated_labels_f1", 
-        "value": 0.8446031029846126, 
-        "severity": 0, 
-        "severity_value": 0.4223015514923063, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8446031029846126%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_scaled isolated_labels_f1", 
-        "value": 0.3314643451064668, 
-        "severity": 0, 
-        "severity_value": -0.3314643451064668, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.3314643451064668%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_scaled isolated_labels_f1", 
-        "value": 0.9264228133342138, 
-        "severity": 0, 
-        "severity_value": 0.4632114066671069, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9264228133342138%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_unscaled isolated_labels_f1", 
-        "value": 0.7343417677163782, 
-        "severity": 0, 
-        "severity_value": -0.7343417677163782, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7343417677163782%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_unscaled isolated_labels_f1", 
-        "value": 0.8678442073794395, 
-        "severity": 0, 
-        "severity_value": 0.43392210368971973, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8678442073794395%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_scaled isolated_labels_f1", 
-        "value": 0.29361156358238616, 
-        "severity": 0, 
-        "severity_value": -0.29361156358238616, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.29361156358238616%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_scaled isolated_labels_f1", 
-        "value": 0.8024761552471181, 
-        "severity": 0, 
-        "severity_value": 0.40123807762355906, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8024761552471181%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_unscaled isolated_labels_f1", 
-        "value": 0.6721931649604577, 
-        "severity": 0, 
-        "severity_value": -0.6721931649604577, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.6721931649604577%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_unscaled isolated_labels_f1", 
-        "value": 0.9249981776786245, 
-        "severity": 0, 
-        "severity_value": 0.46249908883931223, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9249981776786245%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_full_unscaled isolated_labels_f1", 
-        "value": 0.13996298894145387, 
-        "severity": 0, 
-        "severity_value": -0.13996298894145387, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.13996298894145387%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_full_unscaled isolated_labels_f1", 
-        "value": 0.5753464179953096, 
-        "severity": 0, 
-        "severity_value": 0.2876732089976548, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.5753464179953096%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_hvg_unscaled isolated_labels_f1", 
-        "value": 0.10619588885531811, 
-        "severity": 0, 
-        "severity_value": -0.10619588885531811, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.10619588885531811%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_hvg_unscaled isolated_labels_f1", 
-        "value": 0.6116257883184116, 
-        "severity": 0, 
-        "severity_value": 0.3058128941592058, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.6116257883184116%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled isolated_labels_f1", 
-        "value": 0.33236389671405453, 
-        "severity": 0, 
-        "severity_value": -0.33236389671405453, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.33236389671405453%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled isolated_labels_f1", 
-        "value": 0.8339359230481965, 
-        "severity": 0, 
-        "severity_value": 0.41696796152409826, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8339359230481965%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled isolated_labels_f1", 
-        "value": 0.7055050397560596, 
-        "severity": 0, 
-        "severity_value": -0.7055050397560596, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7055050397560596%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled isolated_labels_f1", 
-        "value": 0.8846896616918827, 
-        "severity": 0, 
-        "severity_value": 0.44234483084594134, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8846896616918827%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled isolated_labels_f1", 
-        "value": 0.7948529496267441, 
-        "severity": 0, 
-        "severity_value": -0.7948529496267441, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7948529496267441%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled isolated_labels_f1", 
-        "value": 0.9526293317762197, 
-        "severity": 0, 
-        "severity_value": 0.47631466588810983, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9526293317762197%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled isolated_labels_f1", 
-        "value": 0.7451702063744935, 
-        "severity": 0, 
-        "severity_value": -0.7451702063744935, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7451702063744935%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled isolated_labels_f1", 
-        "value": 0.9261735223761544, 
-        "severity": 0, 
-        "severity_value": 0.4630867611880772, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9261735223761544%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration isolated_labels_f1", 
-        "value": 0.6882387579376034, 
-        "severity": 0, 
-        "severity_value": -0.6882387579376034, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: isolated_labels_f1\n  Worst score: 0.6882387579376034%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration isolated_labels_f1", 
-        "value": 0.7497002260608618, 
-        "severity": 0, 
-        "severity_value": 0.3748501130304309, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: isolated_labels_f1\n  Best score: 0.7497002260608618%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration_batch isolated_labels_f1", 
-        "value": 0.4211763383075842, 
-        "severity": 0, 
-        "severity_value": -0.4211763383075842, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: isolated_labels_f1\n  Worst score: 0.4211763383075842%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration_batch isolated_labels_f1", 
-        "value": 0.4823726297991087, 
-        "severity": 0, 
-        "severity_value": 0.24118631489955436, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: isolated_labels_f1\n  Best score: 0.4823726297991087%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration isolated_labels_f1", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: isolated_labels_f1\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score random_integration isolated_labels_f1", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: isolated_labels_f1\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full isolated_labels_f1", 
-        "value": 0.17614577581446716, 
-        "severity": 0, 
-        "severity_value": -0.17614577581446716, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: isolated_labels_f1\n  Worst score: 0.17614577581446716%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full isolated_labels_f1", 
-        "value": 0.7623467590106198, 
-        "severity": 0, 
-        "severity_value": 0.3811733795053099, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: isolated_labels_f1\n  Best score: 0.7623467590106198%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg isolated_labels_f1", 
-        "value": 0.17963668354406692, 
-        "severity": 0, 
-        "severity_value": -0.17963668354406692, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: isolated_labels_f1\n  Worst score: 0.17963668354406692%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg isolated_labels_f1", 
-        "value": 0.8162707078220767, 
-        "severity": 0, 
-        "severity_value": 0.40813535391103833, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: isolated_labels_f1\n  Best score: 0.8162707078220767%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_scaled isolated_labels_f1", 
-        "value": 0.8406096008133963, 
-        "severity": 0, 
-        "severity_value": -0.8406096008133963, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8406096008133963%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_scaled isolated_labels_f1", 
-        "value": 0.9557055911194526, 
-        "severity": 0, 
-        "severity_value": 0.4778527955597263, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9557055911194526%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_unscaled isolated_labels_f1", 
-        "value": 0.8488986642047812, 
-        "severity": 0, 
-        "severity_value": -0.8488986642047812, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8488986642047812%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_unscaled isolated_labels_f1", 
-        "value": 0.8667897398704985, 
-        "severity": 0, 
-        "severity_value": 0.4333948699352492, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8667897398704985%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_scaled isolated_labels_f1", 
-        "value": 0.8391961457534716, 
-        "severity": 0, 
-        "severity_value": -0.8391961457534716, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8391961457534716%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_scaled isolated_labels_f1", 
-        "value": 0.9522784237544174, 
-        "severity": 0, 
-        "severity_value": 0.4761392118772087, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9522784237544174%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_unscaled isolated_labels_f1", 
-        "value": 0.7324539039506872, 
-        "severity": 0, 
-        "severity_value": -0.7324539039506872, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7324539039506872%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_unscaled isolated_labels_f1", 
-        "value": 0.9531415124050218, 
-        "severity": 0, 
-        "severity_value": 0.4765707562025109, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9531415124050218%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled isolated_labels_f1", 
-        "value": 0.802933693304543, 
-        "severity": 0, 
-        "severity_value": -0.802933693304543, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.802933693304543%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled isolated_labels_f1", 
-        "value": 0.9268136623247477, 
-        "severity": 0, 
-        "severity_value": 0.46340683116237386, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9268136623247477%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled isolated_labels_f1", 
-        "value": 0.7891457216813397, 
-        "severity": 0, 
-        "severity_value": -0.7891457216813397, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7891457216813397%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled isolated_labels_f1", 
-        "value": 0.8518275176295318, 
-        "severity": 0, 
-        "severity_value": 0.4259137588147659, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8518275176295318%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled isolated_labels_f1", 
-        "value": 0.8207900352018215, 
-        "severity": 0, 
-        "severity_value": -0.8207900352018215, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8207900352018215%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled isolated_labels_f1", 
-        "value": 0.9478485252093102, 
-        "severity": 0, 
-        "severity_value": 0.4739242626046551, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9478485252093102%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled isolated_labels_f1", 
-        "value": 0.8258864783937792, 
-        "severity": 0, 
-        "severity_value": -0.8258864783937792, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8258864783937792%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled isolated_labels_f1", 
-        "value": 0.918515607161958, 
-        "severity": 0, 
-        "severity_value": 0.459257803580979, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.918515607161958%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_full_unscaled isolated_labels_f1", 
-        "value": 0.7639325374833101, 
-        "severity": 0, 
-        "severity_value": -0.7639325374833101, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7639325374833101%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_full_unscaled isolated_labels_f1", 
-        "value": 0.940868253228597, 
-        "severity": 0, 
-        "severity_value": 0.4704341266142985, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.940868253228597%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_hvg_unscaled isolated_labels_f1", 
-        "value": 0.8500265368594296, 
-        "severity": 0, 
-        "severity_value": -0.8500265368594296, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8500265368594296%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_hvg_unscaled isolated_labels_f1", 
-        "value": 0.950838566600603, 
-        "severity": 0, 
-        "severity_value": 0.4754192833003015, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.950838566600603%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_full_unscaled isolated_labels_f1", 
-        "value": 0.8112067267851367, 
-        "severity": 0, 
-        "severity_value": -0.8112067267851367, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8112067267851367%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_full_unscaled isolated_labels_f1", 
-        "value": 0.9386357698495329, 
-        "severity": 0, 
-        "severity_value": 0.46931788492476645, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9386357698495329%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_hvg_unscaled isolated_labels_f1", 
-        "value": 0.8306866493707206, 
-        "severity": 0, 
-        "severity_value": -0.8306866493707206, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8306866493707206%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_hvg_unscaled isolated_labels_f1", 
-        "value": 0.9477239335264608, 
-        "severity": 0, 
-        "severity_value": 0.4738619667632304, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9477239335264608%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration isolated_labels_sil", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: isolated_labels_sil\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration isolated_labels_sil", 
-        "value": 0.11385613620374872, 
-        "severity": 0, 
-        "severity_value": 0.05692806810187436, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: isolated_labels_sil\n  Best score: 0.11385613620374872%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_embedding isolated_labels_sil", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_embedding performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: isolated_labels_sil\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_embedding isolated_labels_sil", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_embedding performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: isolated_labels_sil\n  Best score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration isolated_labels_sil", 
-        "value": 0.22414543738304896, 
-        "severity": 0, 
-        "severity_value": -0.22414543738304896, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: isolated_labels_sil\n  Worst score: 0.22414543738304896%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration isolated_labels_sil", 
-        "value": 0.40372789958358213, 
-        "severity": 0, 
-        "severity_value": 0.20186394979179106, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: isolated_labels_sil\n  Best score: 0.40372789958358213%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled isolated_labels_sil", 
-        "value": 0.06086002231431851, 
-        "severity": 0, 
-        "severity_value": -0.06086002231431851, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.06086002231431851%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled isolated_labels_sil", 
-        "value": 0.18612595022340891, 
-        "severity": 0, 
-        "severity_value": 0.09306297511170446, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.18612595022340891%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled isolated_labels_sil", 
-        "value": 0.18632172679831546, 
-        "severity": 0, 
-        "severity_value": -0.18632172679831546, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.18632172679831546%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled isolated_labels_sil", 
-        "value": 0.34697668837010703, 
-        "severity": 0, 
-        "severity_value": 0.17348834418505352, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.34697668837010703%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled isolated_labels_sil", 
-        "value": 0.2638883075242907, 
-        "severity": 0, 
-        "severity_value": -0.2638883075242907, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.2638883075242907%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled isolated_labels_sil", 
-        "value": 0.36339170182779756, 
-        "severity": 0, 
-        "severity_value": 0.18169585091389878, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.36339170182779756%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled isolated_labels_sil", 
-        "value": 0.2191703373506915, 
-        "severity": 0, 
-        "severity_value": -0.2191703373506915, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.2191703373506915%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled isolated_labels_sil", 
-        "value": 0.3529447401046215, 
-        "severity": 0, 
-        "severity_value": 0.17647237005231076, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.3529447401046215%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_scaled isolated_labels_sil", 
-        "value": -0.0059281523374984805, 
-        "severity": 0, 
-        "severity_value": 0.0059281523374984805, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: isolated_labels_sil\n  Worst score: -0.0059281523374984805%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_scaled isolated_labels_sil", 
-        "value": 0.22460513159904874, 
-        "severity": 0, 
-        "severity_value": 0.11230256579952437, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.22460513159904874%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_unscaled isolated_labels_sil", 
-        "value": -0.006198235755091776, 
-        "severity": 0, 
-        "severity_value": 0.006198235755091776, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: -0.006198235755091776%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_unscaled isolated_labels_sil", 
-        "value": 0.22460026534256075, 
-        "severity": 0, 
-        "severity_value": 0.11230013267128038, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.22460026534256075%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_scaled isolated_labels_sil", 
-        "value": 0.027065218498628446, 
-        "severity": 0, 
-        "severity_value": -0.027065218498628446, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.027065218498628446%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_scaled isolated_labels_sil", 
-        "value": 0.28356581621346805, 
-        "severity": 0, 
-        "severity_value": 0.14178290810673402, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.28356581621346805%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_unscaled isolated_labels_sil", 
-        "value": 0.027361384968100533, 
-        "severity": 0, 
-        "severity_value": -0.027361384968100533, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.027361384968100533%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_unscaled isolated_labels_sil", 
-        "value": 0.28356930698503774, 
-        "severity": 0, 
-        "severity_value": 0.14178465349251887, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.28356930698503774%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_scaled isolated_labels_sil", 
-        "value": 0.1515239868714246, 
-        "severity": 0, 
-        "severity_value": -0.1515239868714246, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.1515239868714246%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_scaled isolated_labels_sil", 
-        "value": 0.3407903539505603, 
-        "severity": 0, 
-        "severity_value": 0.17039517697528014, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.3407903539505603%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_unscaled isolated_labels_sil", 
-        "value": 0.19666087665463117, 
-        "severity": 0, 
-        "severity_value": -0.19666087665463117, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.19666087665463117%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_unscaled isolated_labels_sil", 
-        "value": 0.360278439721786, 
-        "severity": 0, 
-        "severity_value": 0.180139219860893, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.360278439721786%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_scaled isolated_labels_sil", 
-        "value": 0.16854874152068347, 
-        "severity": 0, 
-        "severity_value": -0.16854874152068347, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.16854874152068347%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_scaled isolated_labels_sil", 
-        "value": 0.37553454486082094, 
-        "severity": 0, 
-        "severity_value": 0.18776727243041047, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.37553454486082094%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_unscaled isolated_labels_sil", 
-        "value": 0.08639597677949723, 
-        "severity": 0, 
-        "severity_value": -0.08639597677949723, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.08639597677949723%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_unscaled isolated_labels_sil", 
-        "value": 0.38800026658475917, 
-        "severity": 0, 
-        "severity_value": 0.19400013329237958, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.38800026658475917%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_full_unscaled isolated_labels_sil", 
-        "value": -0.14634314861823552, 
-        "severity": 0, 
-        "severity_value": 0.14634314861823552, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: -0.14634314861823552%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_full_unscaled isolated_labels_sil", 
-        "value": 0.1648010844300771, 
-        "severity": 0, 
-        "severity_value": 0.08240054221503855, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.1648010844300771%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_hvg_unscaled isolated_labels_sil", 
-        "value": -0.04421657843697216, 
-        "severity": 0, 
-        "severity_value": 0.04421657843697216, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: -0.04421657843697216%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_hvg_unscaled isolated_labels_sil", 
-        "value": 0.15483571921043904, 
-        "severity": 0, 
-        "severity_value": 0.07741785960521952, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.15483571921043904%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled isolated_labels_sil", 
-        "value": 0.1689574584923211, 
-        "severity": 0, 
-        "severity_value": -0.1689574584923211, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.1689574584923211%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled isolated_labels_sil", 
-        "value": 0.1849231419272197, 
-        "severity": 0, 
-        "severity_value": 0.09246157096360985, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.1849231419272197%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled isolated_labels_sil", 
-        "value": 0.1435505607418182, 
-        "severity": 0, 
-        "severity_value": -0.1435505607418182, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.1435505607418182%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled isolated_labels_sil", 
-        "value": 0.32445552156371193, 
-        "severity": 0, 
-        "severity_value": 0.16222776078185597, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.32445552156371193%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled isolated_labels_sil", 
-        "value": 0.26242030419210216, 
-        "severity": 0, 
-        "severity_value": -0.26242030419210216, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.26242030419210216%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled isolated_labels_sil", 
-        "value": 0.47947044845943987, 
-        "severity": 0, 
-        "severity_value": 0.23973522422971993, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.47947044845943987%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled isolated_labels_sil", 
-        "value": 0.27759617402354125, 
-        "severity": 0, 
-        "severity_value": -0.27759617402354125, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.27759617402354125%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled isolated_labels_sil", 
-        "value": 0.40994706665568725, 
-        "severity": 0, 
-        "severity_value": 0.20497353332784363, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.40994706665568725%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration isolated_labels_sil", 
-        "value": 0.22414542922726063, 
-        "severity": 0, 
-        "severity_value": -0.22414542922726063, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: isolated_labels_sil\n  Worst score: 0.22414542922726063%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration isolated_labels_sil", 
-        "value": 0.40372789958358213, 
-        "severity": 0, 
-        "severity_value": 0.20186394979179106, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: isolated_labels_sil\n  Best score: 0.40372789958358213%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration_batch isolated_labels_sil", 
-        "value": 0.04304352725884225, 
-        "severity": 0, 
-        "severity_value": -0.04304352725884225, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: isolated_labels_sil\n  Worst score: 0.04304352725884225%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration_batch isolated_labels_sil", 
-        "value": 0.10590503355115954, 
-        "severity": 0, 
-        "severity_value": 0.05295251677557977, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: isolated_labels_sil\n  Best score: 0.10590503355115954%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration isolated_labels_sil", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: isolated_labels_sil\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score random_integration isolated_labels_sil", 
-        "value": 0.04469297430114406, 
-        "severity": 0, 
-        "severity_value": 0.02234648715057203, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: isolated_labels_sil\n  Best score: 0.04469297430114406%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full isolated_labels_sil", 
-        "value": 0.17437848558413077, 
-        "severity": 0, 
-        "severity_value": -0.17437848558413077, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: isolated_labels_sil\n  Worst score: 0.17437848558413077%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full isolated_labels_sil", 
-        "value": 0.19098053846965699, 
-        "severity": 0, 
-        "severity_value": 0.09549026923482849, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: isolated_labels_sil\n  Best score: 0.19098053846965699%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg isolated_labels_sil", 
-        "value": 0.20297321907633964, 
-        "severity": 0, 
-        "severity_value": -0.20297321907633964, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: isolated_labels_sil\n  Worst score: 0.20297321907633964%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg isolated_labels_sil", 
-        "value": 0.3017312155406942, 
-        "severity": 0, 
-        "severity_value": 0.1508656077703471, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: isolated_labels_sil\n  Best score: 0.3017312155406942%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_scaled isolated_labels_sil", 
-        "value": 0.22343090373284707, 
-        "severity": 0, 
-        "severity_value": -0.22343090373284707, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.22343090373284707%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_scaled isolated_labels_sil", 
-        "value": 0.3587830971824662, 
-        "severity": 0, 
-        "severity_value": 0.1793915485912331, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.3587830971824662%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_unscaled isolated_labels_sil", 
-        "value": 0.22930157438945573, 
-        "severity": 0, 
-        "severity_value": -0.22930157438945573, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.22930157438945573%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_unscaled isolated_labels_sil", 
-        "value": 0.2934628289616842, 
-        "severity": 0, 
-        "severity_value": 0.1467314144808421, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.2934628289616842%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_scaled isolated_labels_sil", 
-        "value": 0.22179263399936638, 
-        "severity": 0, 
-        "severity_value": -0.22179263399936638, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.22179263399936638%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_scaled isolated_labels_sil", 
-        "value": 0.4627139882931645, 
-        "severity": 0, 
-        "severity_value": 0.23135699414658226, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.4627139882931645%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_unscaled isolated_labels_sil", 
-        "value": 0.2757991717293691, 
-        "severity": 0, 
-        "severity_value": -0.2757991717293691, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.2757991717293691%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_unscaled isolated_labels_sil", 
-        "value": 0.38086805016630987, 
-        "severity": 0, 
-        "severity_value": 0.19043402508315493, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.38086805016630987%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled isolated_labels_sil", 
-        "value": 0.21449687216523183, 
-        "severity": 0, 
-        "severity_value": -0.21449687216523183, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.21449687216523183%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled isolated_labels_sil", 
-        "value": 0.4194261083858102, 
-        "severity": 0, 
-        "severity_value": 0.2097130541929051, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.4194261083858102%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled isolated_labels_sil", 
-        "value": 0.27197785192826773, 
-        "severity": 0, 
-        "severity_value": -0.27197785192826773, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.27197785192826773%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled isolated_labels_sil", 
-        "value": 0.3940821261902086, 
-        "severity": 0, 
-        "severity_value": 0.1970410630951043, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.3940821261902086%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled isolated_labels_sil", 
-        "value": 0.25699917385408855, 
-        "severity": 0, 
-        "severity_value": -0.25699917385408855, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.25699917385408855%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled isolated_labels_sil", 
-        "value": 0.5943092150101407, 
-        "severity": 0, 
-        "severity_value": 0.29715460750507033, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.5943092150101407%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled isolated_labels_sil", 
-        "value": 0.33039349186233363, 
-        "severity": 0, 
-        "severity_value": -0.33039349186233363, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.33039349186233363%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled isolated_labels_sil", 
-        "value": 0.46960716889743087, 
-        "severity": 0, 
-        "severity_value": 0.23480358444871544, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.46960716889743087%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_full_unscaled isolated_labels_sil", 
-        "value": 0.3045953139959251, 
-        "severity": 0, 
-        "severity_value": -0.3045953139959251, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.3045953139959251%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_full_unscaled isolated_labels_sil", 
-        "value": 0.3757412405008055, 
-        "severity": 0, 
-        "severity_value": 0.18787062025040274, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.3757412405008055%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_hvg_unscaled isolated_labels_sil", 
-        "value": 0.31134185833496275, 
-        "severity": 0, 
-        "severity_value": -0.31134185833496275, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.31134185833496275%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_hvg_unscaled isolated_labels_sil", 
-        "value": 0.42796214524150544, 
-        "severity": 0, 
-        "severity_value": 0.21398107262075272, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.42796214524150544%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_full_unscaled isolated_labels_sil", 
-        "value": 0.2491303159332522, 
-        "severity": 0, 
-        "severity_value": -0.2491303159332522, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.2491303159332522%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_full_unscaled isolated_labels_sil", 
-        "value": 0.356951184922772, 
-        "severity": 0, 
-        "severity_value": 0.178475592461386, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.356951184922772%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_hvg_unscaled isolated_labels_sil", 
-        "value": 0.250390930408287, 
-        "severity": 0, 
-        "severity_value": -0.250390930408287, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.250390930408287%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_hvg_unscaled isolated_labels_sil", 
-        "value": 0.3659171366856727, 
-        "severity": 0, 
-        "severity_value": 0.18295856834283636, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.3659171366856727%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration kBET", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: kBET\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration kBET", 
-        "value": 0.043096703955510025, 
-        "severity": 0, 
-        "severity_value": 0.021548351977755013, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: kBET\n  Best score: 0.043096703955510025%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_embedding kBET", 
-        "value": 0.963965830353588, 
-        "severity": 0, 
-        "severity_value": -0.963965830353588, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_embedding performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: kBET\n  Worst score: 0.963965830353588%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_embedding kBET", 
-        "value": 0.988160370707201, 
-        "severity": 0, 
-        "severity_value": 0.4940801853536005, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_embedding performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: kBET\n  Best score: 0.988160370707201%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration kBET", 
-        "value": 0.9914892543189565, 
-        "severity": 0, 
-        "severity_value": -0.9914892543189565, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: kBET\n  Worst score: 0.9914892543189565%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration kBET", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: kBET\n  Best score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled kBET", 
-        "value": 0.12753167713000088, 
-        "severity": 0, 
-        "severity_value": -0.12753167713000088, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: kBET\n  Worst score: 0.12753167713000088%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled kBET", 
-        "value": 0.49044698897051986, 
-        "severity": 0, 
-        "severity_value": 0.24522349448525993, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: kBET\n  Best score: 0.49044698897051986%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled kBET", 
-        "value": 0.030814772398817483, 
-        "severity": 0, 
-        "severity_value": -0.030814772398817483, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: kBET\n  Worst score: 0.030814772398817483%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled kBET", 
-        "value": 0.14161708635862924, 
-        "severity": 0, 
-        "severity_value": 0.07080854317931462, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: kBET\n  Best score: 0.14161708635862924%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled kBET", 
-        "value": 0.0405572034743424, 
-        "severity": 0, 
-        "severity_value": -0.0405572034743424, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: kBET\n  Worst score: 0.0405572034743424%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled kBET", 
-        "value": 0.1908191687630826, 
-        "severity": 0, 
-        "severity_value": 0.0954095843815413, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: kBET\n  Best score: 0.1908191687630826%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled kBET", 
-        "value": 0.012948437925696426, 
-        "severity": 0, 
-        "severity_value": -0.012948437925696426, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: kBET\n  Worst score: 0.012948437925696426%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled kBET", 
-        "value": 0.11541337191206126, 
-        "severity": 0, 
-        "severity_value": 0.05770668595603063, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: kBET\n  Best score: 0.11541337191206126%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_scaled kBET", 
-        "value": 0.1511659943725331, 
-        "severity": 0, 
-        "severity_value": -0.1511659943725331, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: kBET\n  Worst score: 0.1511659943725331%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_scaled kBET", 
-        "value": 0.3357562079524169, 
-        "severity": 0, 
-        "severity_value": 0.16787810397620845, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: kBET\n  Best score: 0.3357562079524169%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_unscaled kBET", 
-        "value": 0.15053439116546113, 
-        "severity": 0, 
-        "severity_value": -0.15053439116546113, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: kBET\n  Worst score: 0.15053439116546113%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_unscaled kBET", 
-        "value": 0.3360741177333343, 
-        "severity": 0, 
-        "severity_value": 0.16803705886666714, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: kBET\n  Best score: 0.3360741177333343%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_scaled kBET", 
-        "value": 0.1552090214776471, 
-        "severity": 0, 
-        "severity_value": -0.1552090214776471, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: kBET\n  Worst score: 0.1552090214776471%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_scaled kBET", 
-        "value": 0.37122092350761243, 
-        "severity": 0, 
-        "severity_value": 0.18561046175380622, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: kBET\n  Best score: 0.37122092350761243%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_unscaled kBET", 
-        "value": 0.1567984347168023, 
-        "severity": 0, 
-        "severity_value": -0.1567984347168023, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: kBET\n  Worst score: 0.1567984347168023%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_unscaled kBET", 
-        "value": 0.370840313711787, 
-        "severity": 0, 
-        "severity_value": 0.1854201568558935, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: kBET\n  Best score: 0.370840313711787%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_scaled kBET", 
-        "value": 0.3262558946833853, 
-        "severity": 0, 
-        "severity_value": -0.3262558946833853, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: kBET\n  Worst score: 0.3262558946833853%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_scaled kBET", 
-        "value": 0.47347393382628067, 
-        "severity": 0, 
-        "severity_value": 0.23673696691314033, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: kBET\n  Best score: 0.47347393382628067%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_unscaled kBET", 
-        "value": 0.10609207222779189, 
-        "severity": 0, 
-        "severity_value": -0.10609207222779189, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: kBET\n  Worst score: 0.10609207222779189%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_unscaled kBET", 
-        "value": 0.4534160313750793, 
-        "severity": 0, 
-        "severity_value": 0.22670801568753965, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: kBET\n  Best score: 0.4534160313750793%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_scaled kBET", 
-        "value": 0.34241818894229625, 
-        "severity": 0, 
-        "severity_value": -0.34241818894229625, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: kBET\n  Worst score: 0.34241818894229625%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_scaled kBET", 
-        "value": 0.5420129032609279, 
-        "severity": 0, 
-        "severity_value": 0.27100645163046394, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: kBET\n  Best score: 0.5420129032609279%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_unscaled kBET", 
-        "value": 0.12039237488367506, 
-        "severity": 0, 
-        "severity_value": -0.12039237488367506, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: kBET\n  Worst score: 0.12039237488367506%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_unscaled kBET", 
-        "value": 0.45464655371063867, 
-        "severity": 0, 
-        "severity_value": 0.22732327685531933, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: kBET\n  Best score: 0.45464655371063867%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_full_unscaled kBET", 
-        "value": 0.12396767098981398, 
-        "severity": 0, 
-        "severity_value": -0.12396767098981398, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: kBET\n  Worst score: 0.12396767098981398%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_full_unscaled kBET", 
-        "value": 0.4179111663929588, 
-        "severity": 0, 
-        "severity_value": 0.2089555831964794, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: kBET\n  Best score: 0.4179111663929588%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_hvg_unscaled kBET", 
-        "value": 0.20894521637206198, 
-        "severity": 0, 
-        "severity_value": -0.20894521637206198, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: kBET\n  Worst score: 0.20894521637206198%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_hvg_unscaled kBET", 
-        "value": 0.5093358713650082, 
-        "severity": 0, 
-        "severity_value": 0.2546679356825041, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: kBET\n  Best score: 0.5093358713650082%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled kBET", 
-        "value": 0.1923854866460028, 
-        "severity": 0, 
-        "severity_value": -0.1923854866460028, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: kBET\n  Worst score: 0.1923854866460028%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled kBET", 
-        "value": 0.41861916852513875, 
-        "severity": 0, 
-        "severity_value": 0.20930958426256938, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: kBET\n  Best score: 0.41861916852513875%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled kBET", 
-        "value": 0.07504586374753879, 
-        "severity": 0, 
-        "severity_value": -0.07504586374753879, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: kBET\n  Worst score: 0.07504586374753879%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled kBET", 
-        "value": 0.1706980393570041, 
-        "severity": 0, 
-        "severity_value": 0.08534901967850204, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: kBET\n  Best score: 0.1706980393570041%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled kBET", 
-        "value": 0.08490445839594009, 
-        "severity": 0, 
-        "severity_value": -0.08490445839594009, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: kBET\n  Worst score: 0.08490445839594009%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled kBET", 
-        "value": 0.13850520626873017, 
-        "severity": 0, 
-        "severity_value": 0.06925260313436508, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: kBET\n  Best score: 0.13850520626873017%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled kBET", 
-        "value": 0.05443579954846016, 
-        "severity": 0, 
-        "severity_value": -0.05443579954846016, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: kBET\n  Worst score: 0.05443579954846016%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled kBET", 
-        "value": 0.1400513183299776, 
-        "severity": 0, 
-        "severity_value": 0.0700256591649888, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: kBET\n  Best score: 0.1400513183299776%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration kBET", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: kBET\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration kBET", 
-        "value": 0.09359200463159185, 
-        "severity": 0, 
-        "severity_value": 0.04679600231579593, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: kBET\n  Best score: 0.09359200463159185%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration_batch kBET", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: kBET\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration_batch kBET", 
-        "value": 0.09856421638475786, 
-        "severity": 0, 
-        "severity_value": 0.04928210819237893, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: kBET\n  Best score: 0.09856421638475786%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration kBET", 
-        "value": 0.21310017856485086, 
-        "severity": 0, 
-        "severity_value": -0.21310017856485086, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: kBET\n  Worst score: 0.21310017856485086%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score random_integration kBET", 
-        "value": 0.3940354481595776, 
-        "severity": 0, 
-        "severity_value": 0.1970177240797888, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: kBET\n  Best score: 0.3940354481595776%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full kBET", 
-        "value": 0.09493954466742946, 
-        "severity": 0, 
-        "severity_value": -0.09493954466742946, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: kBET\n  Worst score: 0.09493954466742946%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full kBET", 
-        "value": 0.30429077489661116, 
-        "severity": 0, 
-        "severity_value": 0.15214538744830558, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: kBET\n  Best score: 0.30429077489661116%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg kBET", 
-        "value": 0.07778912859682882, 
-        "severity": 0, 
-        "severity_value": -0.07778912859682882, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: kBET\n  Worst score: 0.07778912859682882%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg kBET", 
-        "value": 0.2996384834685515, 
-        "severity": 0, 
-        "severity_value": 0.14981924173427574, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: kBET\n  Best score: 0.2996384834685515%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_scaled kBET", 
-        "value": 0.16288956565323567, 
-        "severity": 0, 
-        "severity_value": -0.16288956565323567, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: kBET\n  Worst score: 0.16288956565323567%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_scaled kBET", 
-        "value": 0.2330893429828292, 
-        "severity": 0, 
-        "severity_value": 0.1165446714914146, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: kBET\n  Best score: 0.2330893429828292%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_unscaled kBET", 
-        "value": 0.08215457320090726, 
-        "severity": 0, 
-        "severity_value": -0.08215457320090726, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: kBET\n  Worst score: 0.08215457320090726%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_unscaled kBET", 
-        "value": 0.2012702334331203, 
-        "severity": 0, 
-        "severity_value": 0.10063511671656016, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: kBET\n  Best score: 0.2012702334331203%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_scaled kBET", 
-        "value": 0.1823530897254971, 
-        "severity": 0, 
-        "severity_value": -0.1823530897254971, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: kBET\n  Worst score: 0.1823530897254971%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_scaled kBET", 
-        "value": 0.3063523788460473, 
-        "severity": 0, 
-        "severity_value": 0.15317618942302366, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: kBET\n  Best score: 0.3063523788460473%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_unscaled kBET", 
-        "value": 0.09844472583674165, 
-        "severity": 0, 
-        "severity_value": -0.09844472583674165, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: kBET\n  Worst score: 0.09844472583674165%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_unscaled kBET", 
-        "value": 0.2164468326778661, 
-        "severity": 0, 
-        "severity_value": 0.10822341633893305, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: kBET\n  Best score: 0.2164468326778661%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled kBET", 
-        "value": 0.11522120034279298, 
-        "severity": 0, 
-        "severity_value": -0.11522120034279298, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: kBET\n  Worst score: 0.11522120034279298%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled kBET", 
-        "value": 0.21735395872428812, 
-        "severity": 0, 
-        "severity_value": 0.10867697936214406, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: kBET\n  Best score: 0.21735395872428812%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled kBET", 
-        "value": 0.0737186982232094, 
-        "severity": 0, 
-        "severity_value": -0.0737186982232094, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: kBET\n  Worst score: 0.0737186982232094%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled kBET", 
-        "value": 0.18143051398941817, 
-        "severity": 0, 
-        "severity_value": 0.09071525699470909, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: kBET\n  Best score: 0.18143051398941817%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled kBET", 
-        "value": 0.1399280835899938, 
-        "severity": 0, 
-        "severity_value": -0.1399280835899938, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: kBET\n  Worst score: 0.1399280835899938%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled kBET", 
-        "value": 0.2560304055450364, 
-        "severity": 0, 
-        "severity_value": 0.1280152027725182, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: kBET\n  Best score: 0.2560304055450364%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled kBET", 
-        "value": 0.06484262345944064, 
-        "severity": 0, 
-        "severity_value": -0.06484262345944064, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: kBET\n  Worst score: 0.06484262345944064%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled kBET", 
-        "value": 0.19192231568216633, 
-        "severity": 0, 
-        "severity_value": 0.09596115784108317, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: kBET\n  Best score: 0.19192231568216633%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_full_unscaled kBET", 
-        "value": 0.2088447805507611, 
-        "severity": 0, 
-        "severity_value": -0.2088447805507611, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: kBET\n  Worst score: 0.2088447805507611%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_full_unscaled kBET", 
-        "value": 0.25849606798483254, 
-        "severity": 0, 
-        "severity_value": 0.12924803399241627, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: kBET\n  Best score: 0.25849606798483254%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_hvg_unscaled kBET", 
-        "value": 0.17871292905815284, 
-        "severity": 0, 
-        "severity_value": -0.17871292905815284, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: kBET\n  Worst score: 0.17871292905815284%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_hvg_unscaled kBET", 
-        "value": 0.288579008829194, 
-        "severity": 0, 
-        "severity_value": 0.144289504414597, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: kBET\n  Best score: 0.288579008829194%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_full_unscaled kBET", 
-        "value": 0.21058823955552866, 
-        "severity": 0, 
-        "severity_value": -0.21058823955552866, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: kBET\n  Worst score: 0.21058823955552866%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_full_unscaled kBET", 
-        "value": 0.26431226702579663, 
-        "severity": 0, 
-        "severity_value": 0.13215613351289832, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: kBET\n  Best score: 0.26431226702579663%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_hvg_unscaled kBET", 
-        "value": 0.21968507722918804, 
-        "severity": 0, 
-        "severity_value": -0.21968507722918804, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: kBET\n  Worst score: 0.21968507722918804%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_hvg_unscaled kBET", 
-        "value": 0.2787415773575499, 
-        "severity": 0, 
-        "severity_value": 0.13937078867877495, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: kBET\n  Best score: 0.2787415773575499%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration nmi", 
-        "value": 0.046116728324778665, 
-        "severity": 0, 
-        "severity_value": -0.046116728324778665, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: nmi\n  Worst score: 0.046116728324778665%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration nmi", 
-        "value": 0.3046408063877789, 
-        "severity": 0, 
-        "severity_value": 0.15232040319388945, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: nmi\n  Best score: 0.3046408063877789%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_embedding nmi", 
-        "value": 0.9975559649093183, 
-        "severity": 0, 
-        "severity_value": -0.9975559649093183, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_embedding performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: nmi\n  Worst score: 0.9975559649093183%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_embedding nmi", 
-        "value": 0.9994955718648248, 
-        "severity": 0, 
-        "severity_value": 0.4997477859324124, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_embedding performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: nmi\n  Best score: 0.9994955718648248%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration nmi", 
-        "value": 0.5939640573189124, 
-        "severity": 0, 
-        "severity_value": -0.5939640573189124, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: nmi\n  Worst score: 0.5939640573189124%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration nmi", 
-        "value": 0.6950578437337678, 
-        "severity": 0, 
-        "severity_value": 0.3475289218668839, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: nmi\n  Best score: 0.6950578437337678%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled nmi", 
-        "value": 0.47079203075121734, 
-        "severity": 0, 
-        "severity_value": -0.47079203075121734, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: nmi\n  Worst score: 0.47079203075121734%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled nmi", 
-        "value": 0.7465716111466172, 
-        "severity": 0, 
-        "severity_value": 0.3732858055733086, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: nmi\n  Best score: 0.7465716111466172%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled nmi", 
-        "value": 0.7194350926332633, 
-        "severity": 0, 
-        "severity_value": -0.7194350926332633, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: nmi\n  Worst score: 0.7194350926332633%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled nmi", 
-        "value": 0.9187914743889541, 
-        "severity": 0, 
-        "severity_value": 0.45939573719447707, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: nmi\n  Best score: 0.9187914743889541%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled nmi", 
-        "value": 0.7087036601619395, 
-        "severity": 0, 
-        "severity_value": -0.7087036601619395, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.7087036601619395%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled nmi", 
-        "value": 0.9184731888161737, 
-        "severity": 0, 
-        "severity_value": 0.45923659440808684, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: nmi\n  Best score: 0.9184731888161737%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled nmi", 
-        "value": 0.7080392429286267, 
-        "severity": 0, 
-        "severity_value": -0.7080392429286267, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7080392429286267%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled nmi", 
-        "value": 0.9086825691035177, 
-        "severity": 0, 
-        "severity_value": 0.4543412845517589, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.9086825691035177%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_scaled nmi", 
-        "value": 0.665291374217449, 
-        "severity": 0, 
-        "severity_value": -0.665291374217449, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: nmi\n  Worst score: 0.665291374217449%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_scaled nmi", 
-        "value": 0.8311946292240915, 
-        "severity": 0, 
-        "severity_value": 0.41559731461204574, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: nmi\n  Best score: 0.8311946292240915%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_unscaled nmi", 
-        "value": 0.669032461899621, 
-        "severity": 0, 
-        "severity_value": -0.669032461899621, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: nmi\n  Worst score: 0.669032461899621%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_unscaled nmi", 
-        "value": 0.8321759187584454, 
-        "severity": 0, 
-        "severity_value": 0.4160879593792227, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: nmi\n  Best score: 0.8321759187584454%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_scaled nmi", 
-        "value": 0.7192550945124155, 
-        "severity": 0, 
-        "severity_value": -0.7192550945124155, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.7192550945124155%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_scaled nmi", 
-        "value": 0.8415066521974104, 
-        "severity": 0, 
-        "severity_value": 0.4207533260987052, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: nmi\n  Best score: 0.8415066521974104%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_unscaled nmi", 
-        "value": 0.7303310855678767, 
-        "severity": 0, 
-        "severity_value": -0.7303310855678767, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7303310855678767%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_unscaled nmi", 
-        "value": 0.8756443942484963, 
-        "severity": 0, 
-        "severity_value": 0.43782219712424814, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.8756443942484963%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_scaled nmi", 
-        "value": 0.5551674964813389, 
-        "severity": 0, 
-        "severity_value": -0.5551674964813389, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: nmi\n  Worst score: 0.5551674964813389%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_scaled nmi", 
-        "value": 0.8058805829375192, 
-        "severity": 0, 
-        "severity_value": 0.4029402914687596, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: nmi\n  Best score: 0.8058805829375192%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_unscaled nmi", 
-        "value": 0.6752096925916974, 
-        "severity": 0, 
-        "severity_value": -0.6752096925916974, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: nmi\n  Worst score: 0.6752096925916974%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_unscaled nmi", 
-        "value": 0.862417734790781, 
-        "severity": 0, 
-        "severity_value": 0.4312088673953905, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: nmi\n  Best score: 0.862417734790781%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_scaled nmi", 
-        "value": 0.6253588214886949, 
-        "severity": 0, 
-        "severity_value": -0.6253588214886949, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.6253588214886949%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_scaled nmi", 
-        "value": 0.8755864780244766, 
-        "severity": 0, 
-        "severity_value": 0.4377932390122383, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: nmi\n  Best score: 0.8755864780244766%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_unscaled nmi", 
-        "value": 0.6694648539203124, 
-        "severity": 0, 
-        "severity_value": -0.6694648539203124, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.6694648539203124%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_unscaled nmi", 
-        "value": 0.9169298436578305, 
-        "severity": 0, 
-        "severity_value": 0.45846492182891524, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.9169298436578305%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_full_unscaled nmi", 
-        "value": 0.19161865402270653, 
-        "severity": 0, 
-        "severity_value": -0.19161865402270653, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: nmi\n  Worst score: 0.19161865402270653%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_full_unscaled nmi", 
-        "value": 0.6594435745322571, 
-        "severity": 0, 
-        "severity_value": 0.32972178726612855, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: nmi\n  Best score: 0.6594435745322571%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_hvg_unscaled nmi", 
-        "value": 0.19763558506546067, 
-        "severity": 0, 
-        "severity_value": -0.19763558506546067, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.19763558506546067%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_hvg_unscaled nmi", 
-        "value": 0.7425098537876195, 
-        "severity": 0, 
-        "severity_value": 0.37125492689380973, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.7425098537876195%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled nmi", 
-        "value": 0.5408742179924753, 
-        "severity": 0, 
-        "severity_value": -0.5408742179924753, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: nmi\n  Worst score: 0.5408742179924753%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled nmi", 
-        "value": 0.7778945274427117, 
-        "severity": 0, 
-        "severity_value": 0.38894726372135585, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: nmi\n  Best score: 0.7778945274427117%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled nmi", 
-        "value": 0.7236987858464141, 
-        "severity": 0, 
-        "severity_value": -0.7236987858464141, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: nmi\n  Worst score: 0.7236987858464141%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled nmi", 
-        "value": 0.8677720950362168, 
-        "severity": 0, 
-        "severity_value": 0.4338860475181084, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: nmi\n  Best score: 0.8677720950362168%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled nmi", 
-        "value": 0.7209304397353147, 
-        "severity": 0, 
-        "severity_value": -0.7209304397353147, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.7209304397353147%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled nmi", 
-        "value": 0.9148947759475217, 
-        "severity": 0, 
-        "severity_value": 0.45744738797376083, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: nmi\n  Best score: 0.9148947759475217%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled nmi", 
-        "value": 0.7390023216826311, 
-        "severity": 0, 
-        "severity_value": -0.7390023216826311, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7390023216826311%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled nmi", 
-        "value": 0.8794411255904484, 
-        "severity": 0, 
-        "severity_value": 0.4397205627952242, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.8794411255904484%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration nmi", 
-        "value": 0.5926214543943454, 
-        "severity": 0, 
-        "severity_value": -0.5926214543943454, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: nmi\n  Worst score: 0.5926214543943454%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration nmi", 
-        "value": 0.6967418136982032, 
-        "severity": 0, 
-        "severity_value": 0.3483709068491016, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: nmi\n  Best score: 0.6967418136982032%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration_batch nmi", 
-        "value": 0.41392169321446315, 
-        "severity": 0, 
-        "severity_value": -0.41392169321446315, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: nmi\n  Worst score: 0.41392169321446315%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration_batch nmi", 
-        "value": 0.5596586296203193, 
-        "severity": 0, 
-        "severity_value": 0.27982931481015966, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: nmi\n  Best score: 0.5596586296203193%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration nmi", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: nmi\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score random_integration nmi", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: nmi\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full nmi", 
-        "value": 0.7334411818911505, 
-        "severity": 0, 
-        "severity_value": -0.7334411818911505, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: nmi\n  Worst score: 0.7334411818911505%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full nmi", 
-        "value": 0.8636437374167285, 
-        "severity": 0, 
-        "severity_value": 0.43182186870836425, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: nmi\n  Best score: 0.8636437374167285%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg nmi", 
-        "value": 0.7654389602998317, 
-        "severity": 0, 
-        "severity_value": -0.7654389602998317, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: nmi\n  Worst score: 0.7654389602998317%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg nmi", 
-        "value": 0.9072727391657409, 
-        "severity": 0, 
-        "severity_value": 0.45363636958287046, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: nmi\n  Best score: 0.9072727391657409%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_scaled nmi", 
-        "value": 0.7153021211668502, 
-        "severity": 0, 
-        "severity_value": -0.7153021211668502, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: nmi\n  Worst score: 0.7153021211668502%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_scaled nmi", 
-        "value": 0.8635816344394845, 
-        "severity": 0, 
-        "severity_value": 0.4317908172197423, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: nmi\n  Best score: 0.8635816344394845%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_unscaled nmi", 
-        "value": 0.7021582443603683, 
-        "severity": 0, 
-        "severity_value": -0.7021582443603683, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: nmi\n  Worst score: 0.7021582443603683%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_unscaled nmi", 
-        "value": 0.7752095368894864, 
-        "severity": 0, 
-        "severity_value": 0.3876047684447432, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: nmi\n  Best score: 0.7752095368894864%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_scaled nmi", 
-        "value": 0.7272668874168612, 
-        "severity": 0, 
-        "severity_value": -0.7272668874168612, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.7272668874168612%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_scaled nmi", 
-        "value": 0.9209204008056698, 
-        "severity": 0, 
-        "severity_value": 0.4604602004028349, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: nmi\n  Best score: 0.9209204008056698%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_unscaled nmi", 
-        "value": 0.7325428098452668, 
-        "severity": 0, 
-        "severity_value": -0.7325428098452668, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7325428098452668%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_unscaled nmi", 
-        "value": 0.9303555323581322, 
-        "severity": 0, 
-        "severity_value": 0.4651777661790661, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.9303555323581322%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled nmi", 
-        "value": 0.6655323931393072, 
-        "severity": 0, 
-        "severity_value": -0.6655323931393072, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: nmi\n  Worst score: 0.6655323931393072%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled nmi", 
-        "value": 0.7458529202009321, 
-        "severity": 0, 
-        "severity_value": 0.37292646010046604, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: nmi\n  Best score: 0.7458529202009321%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled nmi", 
-        "value": 0.7041480892645682, 
-        "severity": 0, 
-        "severity_value": -0.7041480892645682, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: nmi\n  Worst score: 0.7041480892645682%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled nmi", 
-        "value": 0.8030797748298105, 
-        "severity": 0, 
-        "severity_value": 0.40153988741490526, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: nmi\n  Best score: 0.8030797748298105%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled nmi", 
-        "value": 0.6804677324903368, 
-        "severity": 0, 
-        "severity_value": -0.6804677324903368, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.6804677324903368%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled nmi", 
-        "value": 0.8848746185227634, 
-        "severity": 0, 
-        "severity_value": 0.4424373092613817, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: nmi\n  Best score: 0.8848746185227634%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled nmi", 
-        "value": 0.7243248852076664, 
-        "severity": 0, 
-        "severity_value": -0.7243248852076664, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7243248852076664%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled nmi", 
-        "value": 0.833251516503162, 
-        "severity": 0, 
-        "severity_value": 0.416625758251581, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.833251516503162%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_full_unscaled nmi", 
-        "value": 0.8016485947394945, 
-        "severity": 0, 
-        "severity_value": -0.8016485947394945, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: nmi\n  Worst score: 0.8016485947394945%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_full_unscaled nmi", 
-        "value": 0.9207489084469125, 
-        "severity": 0, 
-        "severity_value": 0.46037445422345624, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: nmi\n  Best score: 0.9207489084469125%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_hvg_unscaled nmi", 
-        "value": 0.8374523111554194, 
-        "severity": 0, 
-        "severity_value": -0.8374523111554194, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.8374523111554194%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_hvg_unscaled nmi", 
-        "value": 0.9243677927039282, 
-        "severity": 0, 
-        "severity_value": 0.4621838963519641, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.9243677927039282%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_full_unscaled nmi", 
-        "value": 0.7488202492834349, 
-        "severity": 0, 
-        "severity_value": -0.7488202492834349, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: nmi\n  Worst score: 0.7488202492834349%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_full_unscaled nmi", 
-        "value": 0.9128772345683633, 
-        "severity": 0, 
-        "severity_value": 0.45643861728418167, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: nmi\n  Best score: 0.9128772345683633%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_hvg_unscaled nmi", 
-        "value": 0.7371656902770745, 
-        "severity": 0, 
-        "severity_value": -0.7371656902770745, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7371656902770745%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_hvg_unscaled nmi", 
-        "value": 0.9124621792711208, 
-        "severity": 0, 
-        "severity_value": 0.4562310896355604, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.9124621792711208%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration pcr", 
-        "value": 1.908168339763785e-06, 
-        "severity": 0, 
-        "severity_value": -1.908168339763785e-06, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: pcr\n  Worst score: 1.908168339763785e-06%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration pcr", 
-        "value": 3.433011803380945e-05, 
-        "severity": 0, 
-        "severity_value": 1.7165059016904725e-05, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: pcr\n  Best score: 3.433011803380945e-05%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_embedding pcr", 
-        "value": 0.6443719462594208, 
-        "severity": 0, 
-        "severity_value": -0.6443719462594208, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_embedding performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: pcr\n  Worst score: 0.6443719462594208%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_embedding pcr", 
-        "value": 0.9016472062681758, 
-        "severity": 0, 
-        "severity_value": 0.4508236031340879, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_embedding performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: pcr\n  Best score: 0.9016472062681758%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration pcr", 
-        "value": 0.646053474081804, 
-        "severity": 0, 
-        "severity_value": -0.646053474081804, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: pcr\n  Worst score: 0.646053474081804%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration pcr", 
-        "value": 0.9581053719148775, 
-        "severity": 0, 
-        "severity_value": 0.4790526859574388, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: pcr\n  Best score: 0.9581053719148775%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled pcr", 
-        "value": 0.9999999985628375, 
-        "severity": 0, 
-        "severity_value": -0.9999999985628375, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: pcr\n  Worst score: 0.9999999985628375%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled pcr", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: pcr\n  Best score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled pcr", 
-        "value": 0.9999178874611112, 
-        "severity": 0, 
-        "severity_value": -0.9999178874611112, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: pcr\n  Worst score: 0.9999178874611112%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled pcr", 
-        "value": 0.9999964985930198, 
-        "severity": 0, 
-        "severity_value": 0.4999982492965099, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: pcr\n  Best score: 0.9999964985930198%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled pcr", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: pcr\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled pcr", 
-        "value": 1.0000000001022127, 
-        "severity": 0, 
-        "severity_value": 0.5000000000511063, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: pcr\n  Best score: 1.0000000001022127%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled pcr", 
-        "value": 0.9999548129889674, 
-        "severity": 0, 
-        "severity_value": -0.9999548129889674, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: pcr\n  Worst score: 0.9999548129889674%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled pcr", 
-        "value": 0.9999917034470035, 
-        "severity": 0, 
-        "severity_value": 0.49999585172350175, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: pcr\n  Best score: 0.9999917034470035%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_scaled pcr", 
-        "value": 0.6373284655301381, 
-        "severity": 0, 
-        "severity_value": -0.6373284655301381, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: pcr\n  Worst score: 0.6373284655301381%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_scaled pcr", 
-        "value": 0.8642184432906904, 
-        "severity": 0, 
-        "severity_value": 0.4321092216453452, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: pcr\n  Best score: 0.8642184432906904%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_unscaled pcr", 
-        "value": 0.6373380296359447, 
-        "severity": 0, 
-        "severity_value": -0.6373380296359447, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: pcr\n  Worst score: 0.6373380296359447%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_unscaled pcr", 
-        "value": 0.8642270170997669, 
-        "severity": 0, 
-        "severity_value": 0.43211350854988345, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: pcr\n  Best score: 0.8642270170997669%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_scaled pcr", 
-        "value": 0.45241397301394254, 
-        "severity": 0, 
-        "severity_value": -0.45241397301394254, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: pcr\n  Worst score: 0.45241397301394254%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_scaled pcr", 
-        "value": 0.8598975889326267, 
-        "severity": 0, 
-        "severity_value": 0.42994879446631334, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: pcr\n  Best score: 0.8598975889326267%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_unscaled pcr", 
-        "value": 0.45191888963856597, 
-        "severity": 0, 
-        "severity_value": -0.45191888963856597, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: pcr\n  Worst score: 0.45191888963856597%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_unscaled pcr", 
-        "value": 0.8598798614005545, 
-        "severity": 0, 
-        "severity_value": 0.42993993070027725, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: pcr\n  Best score: 0.8598798614005545%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_scaled pcr", 
-        "value": 0.9037714195826285, 
-        "severity": 0, 
-        "severity_value": -0.9037714195826285, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: pcr\n  Worst score: 0.9037714195826285%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_scaled pcr", 
-        "value": 0.96782085760858, 
-        "severity": 0, 
-        "severity_value": 0.48391042880429, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: pcr\n  Best score: 0.96782085760858%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_unscaled pcr", 
-        "value": 0.32250355756377286, 
-        "severity": 0, 
-        "severity_value": -0.32250355756377286, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: pcr\n  Worst score: 0.32250355756377286%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_unscaled pcr", 
-        "value": 0.940244570173553, 
-        "severity": 0, 
-        "severity_value": 0.4701222850867765, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: pcr\n  Best score: 0.940244570173553%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_scaled pcr", 
-        "value": 0.768921693015695, 
-        "severity": 0, 
-        "severity_value": -0.768921693015695, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: pcr\n  Worst score: 0.768921693015695%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_scaled pcr", 
-        "value": 0.9392355892772725, 
-        "severity": 0, 
-        "severity_value": 0.46961779463863623, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: pcr\n  Best score: 0.9392355892772725%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_unscaled pcr", 
-        "value": 0.5589503233459099, 
-        "severity": 0, 
-        "severity_value": -0.5589503233459099, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: pcr\n  Worst score: 0.5589503233459099%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_unscaled pcr", 
-        "value": 0.8818482750113793, 
-        "severity": 0, 
-        "severity_value": 0.44092413750568965, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: pcr\n  Best score: 0.8818482750113793%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_full_unscaled pcr", 
-        "value": 0.8479334235365056, 
-        "severity": 0, 
-        "severity_value": -0.8479334235365056, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: pcr\n  Worst score: 0.8479334235365056%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_full_unscaled pcr", 
-        "value": 0.9452332756851185, 
-        "severity": 0, 
-        "severity_value": 0.47261663784255925, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: pcr\n  Best score: 0.9452332756851185%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_hvg_unscaled pcr", 
-        "value": 0.8241451213963599, 
-        "severity": 0, 
-        "severity_value": -0.8241451213963599, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: pcr\n  Worst score: 0.8241451213963599%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_hvg_unscaled pcr", 
-        "value": 0.9332409498141291, 
-        "severity": 0, 
-        "severity_value": 0.46662047490706454, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: pcr\n  Best score: 0.9332409498141291%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled pcr", 
-        "value": 0.9550704587647098, 
-        "severity": 0, 
-        "severity_value": -0.9550704587647098, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: pcr\n  Worst score: 0.9550704587647098%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled pcr", 
-        "value": 0.9800858500105228, 
-        "severity": 0, 
-        "severity_value": 0.4900429250052614, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: pcr\n  Best score: 0.9800858500105228%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled pcr", 
-        "value": 0.7061983329255073, 
-        "severity": 0, 
-        "severity_value": -0.7061983329255073, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: pcr\n  Worst score: 0.7061983329255073%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled pcr", 
-        "value": 0.8823507658050287, 
-        "severity": 0, 
-        "severity_value": 0.44117538290251435, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: pcr\n  Best score: 0.8823507658050287%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled pcr", 
-        "value": 0.8408923162279704, 
-        "severity": 0, 
-        "severity_value": -0.8408923162279704, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: pcr\n  Worst score: 0.8408923162279704%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled pcr", 
-        "value": 0.9246426677142603, 
-        "severity": 0, 
-        "severity_value": 0.46232133385713015, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: pcr\n  Best score: 0.9246426677142603%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled pcr", 
-        "value": 0.5312692141914828, 
-        "severity": 0, 
-        "severity_value": -0.5312692141914828, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: pcr\n  Worst score: 0.5312692141914828%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled pcr", 
-        "value": 0.864684956537832, 
-        "severity": 0, 
-        "severity_value": 0.432342478268916, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: pcr\n  Best score: 0.864684956537832%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration pcr", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: pcr\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration pcr", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: pcr\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration_batch pcr", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: pcr\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration_batch pcr", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: pcr\n  Best score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration pcr", 
-        "value": 0.9993241202958992, 
-        "severity": 0, 
-        "severity_value": -0.9993241202958992, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: pcr\n  Worst score: 0.9993241202958992%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score random_integration pcr", 
-        "value": 0.9996760956881371, 
-        "severity": 0, 
-        "severity_value": 0.49983804784406854, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: pcr\n  Best score: 0.9996760956881371%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full pcr", 
-        "value": 0.9994911754875365, 
-        "severity": 0, 
-        "severity_value": -0.9994911754875365, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: pcr\n  Worst score: 0.9994911754875365%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full pcr", 
-        "value": 0.9999446013877559, 
-        "severity": 0, 
-        "severity_value": 0.49997230069387794, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: pcr\n  Best score: 0.9999446013877559%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg pcr", 
-        "value": 0.9969149260921096, 
-        "severity": 0, 
-        "severity_value": -0.9969149260921096, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: pcr\n  Worst score: 0.9969149260921096%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg pcr", 
-        "value": 0.9992008346285202, 
-        "severity": 0, 
-        "severity_value": 0.4996004173142601, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: pcr\n  Best score: 0.9992008346285202%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_scaled pcr", 
-        "value": 0.8561300872575691, 
-        "severity": 0, 
-        "severity_value": -0.8561300872575691, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: pcr\n  Worst score: 0.8561300872575691%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_scaled pcr", 
-        "value": 0.9375114983786983, 
-        "severity": 0, 
-        "severity_value": 0.46875574918934915, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: pcr\n  Best score: 0.9375114983786983%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_unscaled pcr", 
-        "value": 0.22409841019663157, 
-        "severity": 0, 
-        "severity_value": -0.22409841019663157, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: pcr\n  Worst score: 0.22409841019663157%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_unscaled pcr", 
-        "value": 0.6891611432481024, 
-        "severity": 0, 
-        "severity_value": 0.3445805716240512, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: pcr\n  Best score: 0.6891611432481024%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_scaled pcr", 
-        "value": 0.8054915499395341, 
-        "severity": 0, 
-        "severity_value": -0.8054915499395341, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: pcr\n  Worst score: 0.8054915499395341%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_scaled pcr", 
-        "value": 0.9135935136701459, 
-        "severity": 0, 
-        "severity_value": 0.4567967568350729, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: pcr\n  Best score: 0.9135935136701459%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_unscaled pcr", 
-        "value": 0.30251273302458814, 
-        "severity": 0, 
-        "severity_value": -0.30251273302458814, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: pcr\n  Worst score: 0.30251273302458814%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_unscaled pcr", 
-        "value": 0.8054984721951918, 
-        "severity": 0, 
-        "severity_value": 0.4027492360975959, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: pcr\n  Best score: 0.8054984721951918%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled pcr", 
-        "value": 0.7534094920331806, 
-        "severity": 0, 
-        "severity_value": -0.7534094920331806, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: pcr\n  Worst score: 0.7534094920331806%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled pcr", 
-        "value": 0.9274254828621921, 
-        "severity": 0, 
-        "severity_value": 0.46371274143109603, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: pcr\n  Best score: 0.9274254828621921%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled pcr", 
-        "value": 0.43040023948540773, 
-        "severity": 0, 
-        "severity_value": -0.43040023948540773, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: pcr\n  Worst score: 0.43040023948540773%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled pcr", 
-        "value": 0.6444309583157669, 
-        "severity": 0, 
-        "severity_value": 0.32221547915788346, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: pcr\n  Best score: 0.6444309583157669%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled pcr", 
-        "value": 0.6420908056221097, 
-        "severity": 0, 
-        "severity_value": -0.6420908056221097, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: pcr\n  Worst score: 0.6420908056221097%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled pcr", 
-        "value": 0.8945258829782321, 
-        "severity": 0, 
-        "severity_value": 0.44726294148911605, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: pcr\n  Best score: 0.8945258829782321%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled pcr", 
-        "value": 0.3001777487858499, 
-        "severity": 0, 
-        "severity_value": -0.3001777487858499, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: pcr\n  Worst score: 0.3001777487858499%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled pcr", 
-        "value": 0.8019841473404011, 
-        "severity": 0, 
-        "severity_value": 0.40099207367020057, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: pcr\n  Best score: 0.8019841473404011%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_full_unscaled pcr", 
-        "value": 0.8767238289941173, 
-        "severity": 0, 
-        "severity_value": -0.8767238289941173, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: pcr\n  Worst score: 0.8767238289941173%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_full_unscaled pcr", 
-        "value": 0.9186149289006977, 
-        "severity": 0, 
-        "severity_value": 0.45930746445034887, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: pcr\n  Best score: 0.9186149289006977%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_hvg_unscaled pcr", 
-        "value": 0.7311473672984079, 
-        "severity": 0, 
-        "severity_value": -0.7311473672984079, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: pcr\n  Worst score: 0.7311473672984079%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_hvg_unscaled pcr", 
-        "value": 0.9139697774337018, 
-        "severity": 0, 
-        "severity_value": 0.4569848887168509, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: pcr\n  Best score: 0.9139697774337018%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_full_unscaled pcr", 
-        "value": 0.8983913140915705, 
-        "severity": 0, 
-        "severity_value": -0.8983913140915705, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: pcr\n  Worst score: 0.8983913140915705%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_full_unscaled pcr", 
-        "value": 0.9360250832011138, 
-        "severity": 0, 
-        "severity_value": 0.4680125416005569, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: pcr\n  Best score: 0.9360250832011138%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_hvg_unscaled pcr", 
-        "value": 0.8193177089224674, 
-        "severity": 0, 
-        "severity_value": -0.8193177089224674, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: pcr\n  Worst score: 0.8193177089224674%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_hvg_unscaled pcr", 
-        "value": 0.9321819321871053, 
-        "severity": 0, 
-        "severity_value": 0.46609096609355266, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: pcr\n  Best score: 0.9321819321871053%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration silhouette", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: silhouette\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration silhouette", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: silhouette\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_embedding silhouette", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_embedding performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: silhouette\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_embedding silhouette", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_embedding performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: silhouette\n  Best score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration silhouette", 
-        "value": 0.13903432310740974, 
-        "severity": 0, 
-        "severity_value": -0.13903432310740974, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: silhouette\n  Worst score: 0.13903432310740974%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration silhouette", 
-        "value": 0.17590136885221508, 
-        "severity": 0, 
-        "severity_value": 0.08795068442610754, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: silhouette\n  Best score: 0.17590136885221508%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled silhouette", 
-        "value": 0.10390410320113075, 
-        "severity": 0, 
-        "severity_value": -0.10390410320113075, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: silhouette\n  Worst score: 0.10390410320113075%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled silhouette", 
-        "value": 0.18203903644193034, 
-        "severity": 0, 
-        "severity_value": 0.09101951822096517, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: silhouette\n  Best score: 0.18203903644193034%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled silhouette", 
-        "value": 0.20384658778453543, 
-        "severity": 0, 
-        "severity_value": -0.20384658778453543, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: silhouette\n  Worst score: 0.20384658778453543%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled silhouette", 
-        "value": 0.28584949868203674, 
-        "severity": 0, 
-        "severity_value": 0.14292474934101837, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: silhouette\n  Best score: 0.28584949868203674%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled silhouette", 
-        "value": 0.23862661330548224, 
-        "severity": 0, 
-        "severity_value": -0.23862661330548224, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: silhouette\n  Worst score: 0.23862661330548224%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled silhouette", 
-        "value": 0.31583519054834547, 
-        "severity": 0, 
-        "severity_value": 0.15791759527417273, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: silhouette\n  Best score: 0.31583519054834547%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled silhouette", 
-        "value": 0.22201767171513076, 
-        "severity": 0, 
-        "severity_value": -0.22201767171513076, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: silhouette\n  Worst score: 0.22201767171513076%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled silhouette", 
-        "value": 0.30468366314152073, 
-        "severity": 0, 
-        "severity_value": 0.15234183157076037, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: silhouette\n  Best score: 0.30468366314152073%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_scaled silhouette", 
-        "value": 0.17177729004272546, 
-        "severity": 0, 
-        "severity_value": -0.17177729004272546, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: silhouette\n  Worst score: 0.17177729004272546%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_scaled silhouette", 
-        "value": 0.4975574722081094, 
-        "severity": 0, 
-        "severity_value": 0.2487787361040547, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: silhouette\n  Best score: 0.4975574722081094%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_unscaled silhouette", 
-        "value": 0.1717706031317736, 
-        "severity": 0, 
-        "severity_value": -0.1717706031317736, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: silhouette\n  Worst score: 0.1717706031317736%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_unscaled silhouette", 
-        "value": 0.49756466711936664, 
-        "severity": 0, 
-        "severity_value": 0.24878233355968332, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: silhouette\n  Best score: 0.49756466711936664%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_scaled silhouette", 
-        "value": 0.27460475741916385, 
-        "severity": 0, 
-        "severity_value": -0.27460475741916385, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: silhouette\n  Worst score: 0.27460475741916385%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_scaled silhouette", 
-        "value": 0.5586328902084471, 
-        "severity": 0, 
-        "severity_value": 0.27931644510422354, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: silhouette\n  Best score: 0.5586328902084471%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_unscaled silhouette", 
-        "value": 0.27461425906213294, 
-        "severity": 0, 
-        "severity_value": -0.27461425906213294, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: silhouette\n  Worst score: 0.27461425906213294%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_unscaled silhouette", 
-        "value": 0.5586329152630466, 
-        "severity": 0, 
-        "severity_value": 0.2793164576315233, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: silhouette\n  Best score: 0.5586329152630466%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_scaled silhouette", 
-        "value": 0.16091944179067694, 
-        "severity": 0, 
-        "severity_value": -0.16091944179067694, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: silhouette\n  Worst score: 0.16091944179067694%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_scaled silhouette", 
-        "value": 0.2596073520635165, 
-        "severity": 0, 
-        "severity_value": 0.12980367603175824, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: silhouette\n  Best score: 0.2596073520635165%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_unscaled silhouette", 
-        "value": 0.13102409508063942, 
-        "severity": 0, 
-        "severity_value": -0.13102409508063942, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: silhouette\n  Worst score: 0.13102409508063942%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_unscaled silhouette", 
-        "value": 0.2500954010487589, 
-        "severity": 0, 
-        "severity_value": 0.12504770052437944, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: silhouette\n  Best score: 0.2500954010487589%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_scaled silhouette", 
-        "value": 0.21632213574331624, 
-        "severity": 0, 
-        "severity_value": -0.21632213574331624, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: silhouette\n  Worst score: 0.21632213574331624%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_scaled silhouette", 
-        "value": 0.372320284174014, 
-        "severity": 0, 
-        "severity_value": 0.186160142087007, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: silhouette\n  Best score: 0.372320284174014%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_unscaled silhouette", 
-        "value": 0.2247315030741713, 
-        "severity": 0, 
-        "severity_value": -0.2247315030741713, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: silhouette\n  Worst score: 0.2247315030741713%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_unscaled silhouette", 
-        "value": 0.36274682863827623, 
-        "severity": 0, 
-        "severity_value": 0.18137341431913812, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: silhouette\n  Best score: 0.36274682863827623%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_full_unscaled silhouette", 
-        "value": 0.003979477430419374, 
-        "severity": 0, 
-        "severity_value": -0.003979477430419374, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: silhouette\n  Worst score: 0.003979477430419374%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_full_unscaled silhouette", 
-        "value": 0.16392909643059145, 
-        "severity": 0, 
-        "severity_value": 0.08196454821529572, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: silhouette\n  Best score: 0.16392909643059145%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_hvg_unscaled silhouette", 
-        "value": 0.009416538645825989, 
-        "severity": 0, 
-        "severity_value": -0.009416538645825989, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: silhouette\n  Worst score: 0.009416538645825989%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_hvg_unscaled silhouette", 
-        "value": 0.23325022997282394, 
-        "severity": 0, 
-        "severity_value": 0.11662511498641197, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: silhouette\n  Best score: 0.23325022997282394%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled silhouette", 
-        "value": 0.08233448869483033, 
-        "severity": 0, 
-        "severity_value": -0.08233448869483033, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: silhouette\n  Worst score: 0.08233448869483033%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled silhouette", 
-        "value": 0.19960251395655457, 
-        "severity": 0, 
-        "severity_value": 0.09980125697827728, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: silhouette\n  Best score: 0.19960251395655457%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled silhouette", 
-        "value": 0.23217868421021448, 
-        "severity": 0, 
-        "severity_value": -0.23217868421021448, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: silhouette\n  Worst score: 0.23217868421021448%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled silhouette", 
-        "value": 0.27161564384156256, 
-        "severity": 0, 
-        "severity_value": 0.13580782192078128, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: silhouette\n  Best score: 0.27161564384156256%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled silhouette", 
-        "value": 0.2575642061346326, 
-        "severity": 0, 
-        "severity_value": -0.2575642061346326, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: silhouette\n  Worst score: 0.2575642061346326%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled silhouette", 
-        "value": 0.31585864416351245, 
-        "severity": 0, 
-        "severity_value": 0.15792932208175622, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: silhouette\n  Best score: 0.31585864416351245%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled silhouette", 
-        "value": 0.2717199806989376, 
-        "severity": 0, 
-        "severity_value": -0.2717199806989376, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: silhouette\n  Worst score: 0.2717199806989376%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled silhouette", 
-        "value": 0.33023590071945935, 
-        "severity": 0, 
-        "severity_value": 0.16511795035972968, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: silhouette\n  Best score: 0.33023590071945935%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration silhouette", 
-        "value": 0.139034326508456, 
-        "severity": 0, 
-        "severity_value": -0.139034326508456, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: silhouette\n  Worst score: 0.139034326508456%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration silhouette", 
-        "value": 0.17590136229115558, 
-        "severity": 0, 
-        "severity_value": 0.08795068114557779, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: silhouette\n  Best score: 0.17590136229115558%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration_batch silhouette", 
-        "value": 0.08964971888883248, 
-        "severity": 0, 
-        "severity_value": -0.08964971888883248, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: silhouette\n  Worst score: 0.08964971888883248%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration_batch silhouette", 
-        "value": 0.1079966103861595, 
-        "severity": 0, 
-        "severity_value": 0.05399830519307975, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: silhouette\n  Best score: 0.1079966103861595%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration silhouette", 
-        "value": 0.026744643459166932, 
-        "severity": 0, 
-        "severity_value": -0.026744643459166932, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: silhouette\n  Worst score: 0.026744643459166932%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score random_integration silhouette", 
-        "value": 0.13425643493193043, 
-        "severity": 0, 
-        "severity_value": 0.06712821746596521, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: silhouette\n  Best score: 0.13425643493193043%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full silhouette", 
-        "value": 0.224732715711349, 
-        "severity": 0, 
-        "severity_value": -0.224732715711349, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: silhouette\n  Worst score: 0.224732715711349%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full silhouette", 
-        "value": 0.2595240590198785, 
-        "severity": 0, 
-        "severity_value": 0.12976202950993926, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: silhouette\n  Best score: 0.2595240590198785%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg silhouette", 
-        "value": 0.253225118138954, 
-        "severity": 0, 
-        "severity_value": -0.253225118138954, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: silhouette\n  Worst score: 0.253225118138954%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg silhouette", 
-        "value": 0.35370746726073854, 
-        "severity": 0, 
-        "severity_value": 0.17685373363036927, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: silhouette\n  Best score: 0.35370746726073854%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_scaled silhouette", 
-        "value": 0.21025442641166467, 
-        "severity": 0, 
-        "severity_value": -0.21025442641166467, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: silhouette\n  Worst score: 0.21025442641166467%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_scaled silhouette", 
-        "value": 0.22141895151324004, 
-        "severity": 0, 
-        "severity_value": 0.11070947575662002, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: silhouette\n  Best score: 0.22141895151324004%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_unscaled silhouette", 
-        "value": 0.1833319823093976, 
-        "severity": 0, 
-        "severity_value": -0.1833319823093976, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: silhouette\n  Worst score: 0.1833319823093976%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_unscaled silhouette", 
-        "value": 0.2181049927506709, 
-        "severity": 0, 
-        "severity_value": 0.10905249637533546, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: silhouette\n  Best score: 0.2181049927506709%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_scaled silhouette", 
-        "value": 0.2308131846768662, 
-        "severity": 0, 
-        "severity_value": -0.2308131846768662, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: silhouette\n  Worst score: 0.2308131846768662%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_scaled silhouette", 
-        "value": 0.31393170134064835, 
-        "severity": 0, 
-        "severity_value": 0.15696585067032418, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: silhouette\n  Best score: 0.31393170134064835%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_unscaled silhouette", 
-        "value": 0.2576224872791697, 
-        "severity": 0, 
-        "severity_value": -0.2576224872791697, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: silhouette\n  Worst score: 0.2576224872791697%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_unscaled silhouette", 
-        "value": 0.3183739753390177, 
-        "severity": 0, 
-        "severity_value": 0.15918698766950884, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: silhouette\n  Best score: 0.3183739753390177%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled silhouette", 
-        "value": 0.18637460420666405, 
-        "severity": 0, 
-        "severity_value": -0.18637460420666405, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: silhouette\n  Worst score: 0.18637460420666405%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled silhouette", 
-        "value": 0.23413513504576286, 
-        "severity": 0, 
-        "severity_value": 0.11706756752288143, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: silhouette\n  Best score: 0.23413513504576286%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled silhouette", 
-        "value": 0.22728651086582313, 
-        "severity": 0, 
-        "severity_value": -0.22728651086582313, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: silhouette\n  Worst score: 0.22728651086582313%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled silhouette", 
-        "value": 0.30719507734193513, 
-        "severity": 0, 
-        "severity_value": 0.15359753867096756, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: silhouette\n  Best score: 0.30719507734193513%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled silhouette", 
-        "value": 0.24965124213330145, 
-        "severity": 0, 
-        "severity_value": -0.24965124213330145, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: silhouette\n  Worst score: 0.24965124213330145%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled silhouette", 
-        "value": 0.3868076530044166, 
-        "severity": 0, 
-        "severity_value": 0.1934038265022083, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: silhouette\n  Best score: 0.3868076530044166%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled silhouette", 
-        "value": 0.28719504894116155, 
-        "severity": 0, 
-        "severity_value": -0.28719504894116155, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: silhouette\n  Worst score: 0.28719504894116155%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled silhouette", 
-        "value": 0.36323032137785527, 
-        "severity": 0, 
-        "severity_value": 0.18161516068892763, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: silhouette\n  Best score: 0.36323032137785527%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_full_unscaled silhouette", 
-        "value": 0.2505535763998079, 
-        "severity": 0, 
-        "severity_value": -0.2505535763998079, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: silhouette\n  Worst score: 0.2505535763998079%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_full_unscaled silhouette", 
-        "value": 0.2898788413756789, 
-        "severity": 0, 
-        "severity_value": 0.14493942068783944, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: silhouette\n  Best score: 0.2898788413756789%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_hvg_unscaled silhouette", 
-        "value": 0.2742699155900445, 
-        "severity": 0, 
-        "severity_value": -0.2742699155900445, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: silhouette\n  Worst score: 0.2742699155900445%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_hvg_unscaled silhouette", 
-        "value": 0.32342597190209915, 
-        "severity": 0, 
-        "severity_value": 0.16171298595104958, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: silhouette\n  Best score: 0.32342597190209915%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_full_unscaled silhouette", 
-        "value": 0.2083156161249241, 
-        "severity": 0, 
-        "severity_value": -0.2083156161249241, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: silhouette\n  Worst score: 0.2083156161249241%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_full_unscaled silhouette", 
-        "value": 0.23192214150715584, 
-        "severity": 0, 
-        "severity_value": 0.11596107075357792, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: silhouette\n  Best score: 0.23192214150715584%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_hvg_unscaled silhouette", 
-        "value": 0.21592401221564542, 
-        "severity": 0, 
-        "severity_value": -0.21592401221564542, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: silhouette\n  Worst score: 0.21592401221564542%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_hvg_unscaled silhouette", 
-        "value": 0.2319303697324993, 
-        "severity": 0, 
-        "severity_value": 0.11596518486624965, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: silhouette\n  Best score: 0.2319303697324993%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration silhouette_batch", 
-        "value": 0.4054259450381344, 
-        "severity": 0, 
-        "severity_value": -0.4054259450381344, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: silhouette_batch\n  Worst score: 0.4054259450381344%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration silhouette_batch", 
-        "value": 0.4929950788349831, 
-        "severity": 0, 
-        "severity_value": 0.24649753941749156, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: batch_random_integration\n  Metric id: silhouette_batch\n  Best score: 0.4929950788349831%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_embedding silhouette_batch", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_embedding performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: silhouette_batch\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_embedding silhouette_batch", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_embedding performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_embedding\n  Metric id: silhouette_batch\n  Best score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration silhouette_batch", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: silhouette_batch\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration silhouette_batch", 
-        "value": 0.6490172427244232, 
-        "severity": 0, 
-        "severity_value": 0.3245086213622116, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: celltype_random_integration\n  Metric id: silhouette_batch\n  Best score: 0.6490172427244232%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled silhouette_batch", 
-        "value": 0.643233640844541, 
-        "severity": 0, 
-        "severity_value": -0.643233640844541, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.643233640844541%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled silhouette_batch", 
-        "value": 0.7478993981782965, 
-        "severity": 0, 
-        "severity_value": 0.37394969908914827, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_scaled\n  Metric id: silhouette_batch\n  Best score: 0.7478993981782965%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled silhouette_batch", 
-        "value": 0.5692326730787858, 
-        "severity": 0, 
-        "severity_value": -0.5692326730787858, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.5692326730787858%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled silhouette_batch", 
-        "value": 0.64610008352597, 
-        "severity": 0, 
-        "severity_value": 0.323050041762985, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_full_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.64610008352597%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled silhouette_batch", 
-        "value": 0.4609814036265471, 
-        "severity": 0, 
-        "severity_value": -0.4609814036265471, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.4609814036265471%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled silhouette_batch", 
-        "value": 0.5110670531624513, 
-        "severity": 0, 
-        "severity_value": 0.25553352658122563, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_scaled\n  Metric id: silhouette_batch\n  Best score: 0.5110670531624513%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled silhouette_batch", 
-        "value": 0.5102701875708178, 
-        "severity": 0, 
-        "severity_value": -0.5102701875708178, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.5102701875708178%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled silhouette_batch", 
-        "value": 0.6153069256920451, 
-        "severity": 0, 
-        "severity_value": 0.3076534628460226, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: combat_hvg_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.6153069256920451%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_scaled silhouette_batch", 
-        "value": 0.2528292590011704, 
-        "severity": 0, 
-        "severity_value": -0.2528292590011704, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.2528292590011704%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_scaled silhouette_batch", 
-        "value": 0.4642817043521894, 
-        "severity": 0, 
-        "severity_value": 0.2321408521760947, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_scaled\n  Metric id: silhouette_batch\n  Best score: 0.4642817043521894%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_unscaled silhouette_batch", 
-        "value": 0.2528163197838626, 
-        "severity": 0, 
-        "severity_value": -0.2528163197838626, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.2528163197838626%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_unscaled silhouette_batch", 
-        "value": 0.464330981534931, 
-        "severity": 0, 
-        "severity_value": 0.2321654907674655, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.464330981534931%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_scaled silhouette_batch", 
-        "value": 0.17528458884886391, 
-        "severity": 0, 
-        "severity_value": -0.17528458884886391, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.17528458884886391%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_scaled silhouette_batch", 
-        "value": 0.4603220203884302, 
-        "severity": 0, 
-        "severity_value": 0.2301610101942151, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: silhouette_batch\n  Best score: 0.4603220203884302%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_unscaled silhouette_batch", 
-        "value": 0.17528308403731094, 
-        "severity": 0, 
-        "severity_value": -0.17528308403731094, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.17528308403731094%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_unscaled silhouette_batch", 
-        "value": 0.46035174698272474, 
-        "severity": 0, 
-        "severity_value": 0.23017587349136237, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.46035174698272474%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_scaled silhouette_batch", 
-        "value": 0.5052434651768073, 
-        "severity": 0, 
-        "severity_value": -0.5052434651768073, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.5052434651768073%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_scaled silhouette_batch", 
-        "value": 0.6123824997776183, 
-        "severity": 0, 
-        "severity_value": 0.30619124988880914, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_scaled\n  Metric id: silhouette_batch\n  Best score: 0.6123824997776183%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_unscaled silhouette_batch", 
-        "value": 0.4839047358984902, 
-        "severity": 0, 
-        "severity_value": -0.4839047358984902, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.4839047358984902%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_unscaled silhouette_batch", 
-        "value": 0.6031090402216434, 
-        "severity": 0, 
-        "severity_value": 0.3015545201108217, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_full_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.6031090402216434%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_scaled silhouette_batch", 
-        "value": 0.5062157673361474, 
-        "severity": 0, 
-        "severity_value": -0.5062157673361474, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.5062157673361474%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_scaled silhouette_batch", 
-        "value": 0.6320907137170152, 
-        "severity": 0, 
-        "severity_value": 0.3160453568585076, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_scaled\n  Metric id: silhouette_batch\n  Best score: 0.6320907137170152%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_unscaled silhouette_batch", 
-        "value": 0.4885554545431545, 
-        "severity": 0, 
-        "severity_value": -0.4885554545431545, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.4885554545431545%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_unscaled silhouette_batch", 
-        "value": 0.5359712019414504, 
-        "severity": 0, 
-        "severity_value": 0.2679856009707252, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: harmony_hvg_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.5359712019414504%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_full_unscaled silhouette_batch", 
-        "value": 0.17342492502481358, 
-        "severity": 0, 
-        "severity_value": -0.17342492502481358, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.17342492502481358%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_full_unscaled silhouette_batch", 
-        "value": 0.40788627138677225, 
-        "severity": 0, 
-        "severity_value": 0.20394313569338612, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_full_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.40788627138677225%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score liger_hvg_unscaled silhouette_batch", 
-        "value": 0.005924706212446052, 
-        "severity": 0, 
-        "severity_value": -0.005924706212446052, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.005924706212446052%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score liger_hvg_unscaled silhouette_batch", 
-        "value": 0.37534272746269204, 
-        "severity": 0, 
-        "severity_value": 0.18767136373134602, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: liger_hvg_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.37534272746269204%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled silhouette_batch", 
-        "value": 0.7020131824691218, 
-        "severity": 0, 
-        "severity_value": -0.7020131824691218, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.7020131824691218%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled silhouette_batch", 
-        "value": 0.7668492242543122, 
-        "severity": 0, 
-        "severity_value": 0.3834246121271561, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_scaled\n  Metric id: silhouette_batch\n  Best score: 0.7668492242543122%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled silhouette_batch", 
-        "value": 0.6011479300606974, 
-        "severity": 0, 
-        "severity_value": -0.6011479300606974, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.6011479300606974%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled silhouette_batch", 
-        "value": 0.6308109166720193, 
-        "severity": 0, 
-        "severity_value": 0.31540545833600964, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_full_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.6308109166720193%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled silhouette_batch", 
-        "value": 0.6374266800414252, 
-        "severity": 0, 
-        "severity_value": -0.6374266800414252, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.6374266800414252%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled silhouette_batch", 
-        "value": 0.6444780322011204, 
-        "severity": 0, 
-        "severity_value": 0.3222390161005602, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_scaled\n  Metric id: silhouette_batch\n  Best score: 0.6444780322011204%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled silhouette_batch", 
-        "value": 0.5959107275590428, 
-        "severity": 0, 
-        "severity_value": -0.5959107275590428, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.5959107275590428%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled silhouette_batch", 
-        "value": 0.6440143145509026, 
-        "severity": 0, 
-        "severity_value": 0.3220071572754513, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: mnn_hvg_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.6440143145509026%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration silhouette_batch", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: silhouette_batch\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration silhouette_batch", 
-        "value": 0.2985983904259605, 
-        "severity": 0, 
-        "severity_value": 0.14929919521298024, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration\n  Metric id: silhouette_batch\n  Best score: 0.2985983904259605%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration_batch silhouette_batch", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration_batch performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: silhouette_batch\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score no_integration_batch silhouette_batch", 
-        "value": 0.3328268303096647, 
-        "severity": 0, 
-        "severity_value": 0.16641341515483235, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration_batch performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: no_integration_batch\n  Metric id: silhouette_batch\n  Best score: 0.3328268303096647%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration silhouette_batch", 
-        "value": 0.18297246559244262, 
-        "severity": 0, 
-        "severity_value": -0.18297246559244262, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: silhouette_batch\n  Worst score: 0.18297246559244262%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score random_integration silhouette_batch", 
-        "value": 0.6172005037911463, 
-        "severity": 0, 
-        "severity_value": 0.30860025189557316, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: random_integration\n  Metric id: silhouette_batch\n  Best score: 0.6172005037911463%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full silhouette_batch", 
-        "value": 0.5045998667902337, 
-        "severity": 0, 
-        "severity_value": -0.5045998667902337, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: silhouette_batch\n  Worst score: 0.5045998667902337%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full silhouette_batch", 
-        "value": 0.5304317225993253, 
-        "severity": 0, 
-        "severity_value": 0.2652158612996626, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_full\n  Metric id: silhouette_batch\n  Best score: 0.5304317225993253%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg silhouette_batch", 
-        "value": 0.48123352793911006, 
-        "severity": 0, 
-        "severity_value": -0.48123352793911006, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: silhouette_batch\n  Worst score: 0.48123352793911006%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg silhouette_batch", 
-        "value": 0.6070225924872547, 
-        "severity": 0, 
-        "severity_value": 0.3035112962436273, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scalex_hvg\n  Metric id: silhouette_batch\n  Best score: 0.6070225924872547%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_scaled silhouette_batch", 
-        "value": 0.6970785926987316, 
-        "severity": 0, 
-        "severity_value": -0.6970785926987316, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.6970785926987316%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_scaled silhouette_batch", 
-        "value": 0.7597840348392353, 
-        "severity": 0, 
-        "severity_value": 0.37989201741961764, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_scaled\n  Metric id: silhouette_batch\n  Best score: 0.7597840348392353%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_unscaled silhouette_batch", 
-        "value": 0.6219666557841588, 
-        "severity": 0, 
-        "severity_value": -0.6219666557841588, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.6219666557841588%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_unscaled silhouette_batch", 
-        "value": 0.6711911667787916, 
-        "severity": 0, 
-        "severity_value": 0.3355955833893958, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_full_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.6711911667787916%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_scaled silhouette_batch", 
-        "value": 0.6320643446732024, 
-        "severity": 0, 
-        "severity_value": -0.6320643446732024, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.6320643446732024%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_scaled silhouette_batch", 
-        "value": 0.7412205099579292, 
-        "severity": 0, 
-        "severity_value": 0.3706102549789646, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: silhouette_batch\n  Best score: 0.7412205099579292%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_unscaled silhouette_batch", 
-        "value": 0.66240750717132, 
-        "severity": 0, 
-        "severity_value": -0.66240750717132, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.66240750717132%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_unscaled silhouette_batch", 
-        "value": 0.7143465404664868, 
-        "severity": 0, 
-        "severity_value": 0.3571732702332434, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.7143465404664868%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled silhouette_batch", 
-        "value": 0.29112296421336303, 
-        "severity": 0, 
-        "severity_value": -0.29112296421336303, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.29112296421336303%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled silhouette_batch", 
-        "value": 0.4693596995944813, 
-        "severity": 0, 
-        "severity_value": 0.23467984979724066, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_scaled\n  Metric id: silhouette_batch\n  Best score: 0.4693596995944813%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled silhouette_batch", 
-        "value": 0.38971214771035945, 
-        "severity": 0, 
-        "severity_value": -0.38971214771035945, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.38971214771035945%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled silhouette_batch", 
-        "value": 0.6243189046674522, 
-        "severity": 0, 
-        "severity_value": 0.3121594523337261, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_full_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.6243189046674522%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled silhouette_batch", 
-        "value": 0.1990569672022636, 
-        "severity": 0, 
-        "severity_value": -0.1990569672022636, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.1990569672022636%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled silhouette_batch", 
-        "value": 0.46135052476212923, 
-        "severity": 0, 
-        "severity_value": 0.23067526238106462, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: silhouette_batch\n  Best score: 0.46135052476212923%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled silhouette_batch", 
-        "value": 0.3403432809017445, 
-        "severity": 0, 
-        "severity_value": -0.3403432809017445, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.3403432809017445%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled silhouette_batch", 
-        "value": 0.42667966207961344, 
-        "severity": 0, 
-        "severity_value": 0.21333983103980672, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.42667966207961344%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_full_unscaled silhouette_batch", 
-        "value": 0.4982719272026826, 
-        "severity": 0, 
-        "severity_value": -0.4982719272026826, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.4982719272026826%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_full_unscaled silhouette_batch", 
-        "value": 0.5653911811034231, 
-        "severity": 0, 
-        "severity_value": 0.28269559055171156, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_full_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.5653911811034231%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_hvg_unscaled silhouette_batch", 
-        "value": 0.4909170033458638, 
-        "severity": 0, 
-        "severity_value": -0.4909170033458638, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.4909170033458638%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_hvg_unscaled silhouette_batch", 
-        "value": 0.5589250393287365, 
-        "severity": 0, 
-        "severity_value": 0.27946251966436825, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scanvi_hvg_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.5589250393287365%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_full_unscaled silhouette_batch", 
-        "value": 0.5053880092654622, 
-        "severity": 0, 
-        "severity_value": -0.5053880092654622, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.5053880092654622%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_full_unscaled silhouette_batch", 
-        "value": 0.5794891377300293, 
-        "severity": 0, 
-        "severity_value": 0.28974456886501465, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_full_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.5794891377300293%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_hvg_unscaled silhouette_batch", 
-        "value": 0.515941711799034, 
-        "severity": 0, 
-        "severity_value": -0.515941711799034, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.515941711799034%\n"
-    }, 
-    {
-        "task_id": "batch_integration_embed", 
-        "category": "Scaling", 
-        "name": "Best score scvi_hvg_unscaled silhouette_batch", 
-        "value": 0.5904660615321738, 
-        "severity": 0, 
-        "severity_value": 0.2952330307660869, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_embed\n  Method id: scvi_hvg_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.5904660615321738%\n"
-    }
-]
\ No newline at end of file
diff --git a/results/batch_integration_embed/data/results.json b/results/batch_integration_embed/data/results.json
deleted file mode 100644
index 92d722c0..00000000
--- a/results/batch_integration_embed/data/results.json
+++ /dev/null
@@ -1,4802 +0,0 @@
-[
-    {
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-        "commit_sha": "65efdc87e3f4048b94b98c6f9fbfe10dae8d5ab0", 
-        "method_id": "no_integration", 
-        "dataset_id": "pancreas_batch", 
-        "submission_time": "2023-02-21 18:03:03.397", 
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-        "resources": {
-            "duration_sec": 360.0, 
-            "cpu_pct": 16.7, 
-            "peak_memory_mb": 1500.0, 
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-            "silhouette_batch": 0.0
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-        "submission_time": "2023-02-21 18:03:03.463", 
-        "code_version": "0.7.0", 
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-            "cpu_pct": 80.9, 
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-            "disk_read_mb": 1300.0, 
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-        "scaled_scores": {
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-            "isolated_labels_f1": 0.0, 
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-            "silhouette_batch": 0.6172005037911463
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-    {
-        "task_id": "batch_integration_embed", 
-        "commit_sha": "65efdc87e3f4048b94b98c6f9fbfe10dae8d5ab0", 
-        "method_id": "batch_random_integration", 
-        "dataset_id": "pancreas_batch", 
-        "submission_time": "2023-02-21 18:03:02.847", 
-        "code_version": "0.7.0", 
-        "resources": {
-            "duration_sec": 380.0, 
-            "cpu_pct": 90.1, 
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-            "disk_read_mb": 1300.0, 
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-    {
-        "task_id": "batch_integration_embed", 
-        "commit_sha": "65efdc87e3f4048b94b98c6f9fbfe10dae8d5ab0", 
-        "method_id": "scvi_hvg_unscaled", 
-        "dataset_id": "immune_batch", 
-        "submission_time": "2023-02-21 23:05:12.673", 
-        "code_version": "0.20.1", 
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-    {
-        "task_id": "batch_integration_embed", 
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-        "method_id": "scvi_full_unscaled", 
-        "dataset_id": "immune_batch", 
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-        "code_version": "0.20.1", 
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-        "method_id": "scanvi_full_unscaled", 
-        "dataset_id": "immune_batch", 
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-        "code_version": "0.20.1", 
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-        "method_id": "liger_full_unscaled", 
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-            "silhouette_batch": 0.28646482088147485
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-        "mean_score": 0.4758143609321053
-    }
-]
\ No newline at end of file
diff --git a/results/batch_integration_embed/data/task_info.json b/results/batch_integration_embed/data/task_info.json
deleted file mode 100644
index 05b16889..00000000
--- a/results/batch_integration_embed/data/task_info.json
+++ /dev/null
@@ -1,68 +0,0 @@
-{
-  "task_id": "batch_integration_embed",
-  "commit_sha": "c97decf07adb2e3050561d6fa9ae46132be07bef",
-  "task_name": "Batch integration embed",
-  "task_summary": "Removing batch effects while preserving biological variation (embedding output)",
-  "task_description": "\nThis is a sub-task of the overall batch integration task. Batch (or data) integration\nintegrates datasets across batches that arise from various biological and technical\nsources. Methods that integrate batches typically have three different types of output:\na corrected feature matrix, a joint embedding across batches, and/or an integrated\ncell-cell similarity graph (e.g., a kNN graph). This sub-task focuses on all methods\nthat can output joint embeddings, and includes methods that canonically output corrected\nfeature matrices with subsequent postprocessing to generate a joint embedding. Other\nsub-tasks for batch integration can be found for:\n\n* [graphs](../batch_integration_graph/), and\n* [corrected features](../batch_integration_feature/)\n\nThis sub-task was taken from a\n[benchmarking study of data integration\nmethods](https://openproblems.bio/bibliography#luecken2022benchmarking).\n\n",
-  "repo": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed",
-  "authors": [
-    {
-      "name": "Michaela Mueller",
-      "roles": ["maintainer", "author"],
-      "info": {
-        "github": "mumichae",
-        "orcid": "0000-0002-1401-1785"
-      }
-    },
-    {
-      "name": "Malte Luecken",
-      "roles": "author",
-      "info": {
-        "github": "LuckyMD",
-        "orcid": "0000-0001-7464-7921"
-      }
-    },
-    {
-      "name": "Daniel Strobl",
-      "roles": "author",
-      "info": {
-        "github": "danielStrobl",
-        "orcid": "0000-0002-5516-7057"
-      }
-    },
-    {
-      "name": "Robrecht Cannoodt",
-      "roles": "contributor",
-      "info": {
-        "github": "rcannood",
-        "orcid": "0000-0003-3641-729X"
-      }
-    },
-    {
-      "name": "Scott Gigante",
-      "roles": "contributor",
-      "info": {
-        "github": "scottgigante",
-        "orcid": "0000-0002-4544-2764"
-      }
-    },
-    {
-      "name": "Kai Waldrant",
-      "roles": "contributor",
-      "info": {
-        "github": "KaiWaldrant",
-        "orcid": "0009-0003-8555-1361"
-      }
-    },
-    {
-      "name": "Nartin Kim",
-      "roles": "contributor",
-      "info": {
-        "github": "martinkim0",
-        "orcid": "0009-0003-8555-1361"
-      }
-    }
-  ],
-  "version": "v1.0.0",
-  "license": "MIT"
-}
diff --git a/results/batch_integration_embed/index.markdown_strict_files/figure-markdown_strict/raw_results-1.png b/results/batch_integration_embed/index.markdown_strict_files/figure-markdown_strict/raw_results-1.png
deleted file mode 100644
index 110f0152..00000000
Binary files a/results/batch_integration_embed/index.markdown_strict_files/figure-markdown_strict/raw_results-1.png and /dev/null differ
diff --git a/results/batch_integration_embed/index.markdown_strict_files/figure-markdown_strict/summary-1.png b/results/batch_integration_embed/index.markdown_strict_files/figure-markdown_strict/summary-1.png
deleted file mode 100644
index fa7d93ad..00000000
Binary files a/results/batch_integration_embed/index.markdown_strict_files/figure-markdown_strict/summary-1.png and /dev/null differ
diff --git a/results/batch_integration_embed/index.qmd b/results/batch_integration_embed/index.qmd
deleted file mode 100644
index cde052cd..00000000
--- a/results/batch_integration_embed/index.qmd
+++ /dev/null
@@ -1,22 +0,0 @@
----
-title: "Batch integration embed"
-subtitle: "Removing batch effects while preserving biological variation (embedding output)"
-image: thumbnail.svg
-page-layout: full
-css: ../_include/task_template.css
-engine: knitr
-fig-cap-location: bottom
-citation-location: document
-bibliography: 
-  - library.bib
-  - ../../library.bib
-toc: false
----
-
-```{r}
-#| include: false
-params <- list(data_dir = "results/batch_integration_embed/data")
-params <- list(data_dir = "./data")
-```
-
-{{< include ../_include/_task_template.qmd >}}
diff --git a/results/batch_integration_feature/data/dataset_info.json b/results/batch_integration_feature/data/dataset_info.json
deleted file mode 100644
index d8171da4..00000000
--- a/results/batch_integration_feature/data/dataset_info.json
+++ /dev/null
@@ -1,38 +0,0 @@
-[
-    {
-        "dataset_name": "Immune (by batch)",
-        "image": "openproblems",
-        "data_url": "https://ndownloader.figshare.com/files/36086786",
-        "data_reference": "luecken2022benchmarking",
-        "dataset_summary": "Human immune cells from peripheral blood and bone marrow taken from 5 datasets comprising 10 batches across technologies (10X, Smart-seq2).",
-        "task_id": "batch_integration_feature",
-        "commit_sha": "9d1665076cf6215a31f89ed2be8be20a02502887",
-        "dataset_id": "immune_batch",
-        "source_dataset_id": "openproblems_v1/immune_cells",
-        "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/_batch_integration/_common/datasets/immune.py"
-    },
-    {
-        "dataset_name": "Lung (Viera Braga et al.)",
-        "image": "openproblems",
-        "data_url": "https://figshare.com/ndownloader/files/24539942",
-        "data_reference": "luecken2022benchmarking",
-        "dataset_summary": "Human lung scRNA-seq data from 3 datasets with 32,472 cells. From Vieira Braga et al. Technologies: 10X and Drop-seq.",
-        "task_id": "batch_integration_feature",
-        "commit_sha": "9d1665076cf6215a31f89ed2be8be20a02502887",
-        "dataset_id": "lung_batch",
-        "source_dataset_id": "openproblems_v1/tabula_muris_senis_droplet_lung",
-        "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/_batch_integration/_common/datasets/lung.py"
-    },
-    {
-        "dataset_name": "Pancreas (by batch)",
-        "image": "openproblems",
-        "data_url": "https://ndownloader.figshare.com/files/36086813",
-        "data_reference": "luecken2022benchmarking",
-        "dataset_summary": "Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq).",
-        "task_id": "batch_integration_feature",
-        "commit_sha": "9d1665076cf6215a31f89ed2be8be20a02502887",
-        "dataset_id": "pancreas_batch",
-        "source_dataset_id": "openproblems_v1/pancreas",
-        "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/_batch_integration/_common/datasets/pancreas.py"
-    }
-]
\ No newline at end of file
diff --git a/results/batch_integration_feature/data/method_info.json b/results/batch_integration_feature/data/method_info.json
deleted file mode 100644
index 90b037cb..00000000
--- a/results/batch_integration_feature/data/method_info.json
+++ /dev/null
@@ -1,377 +0,0 @@
-[
-  {
-    "method_name": "Random Integration by Batch",
-    "method_summary": "Feature values, embedding coordinates, and graph connectivity are all randomly permuted within each batch label",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
-    "is_baseline": true,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_feature",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "batch_random_integration",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/_common/methods/baseline.py"
-  },
-  {
-    "method_name": "Random Embedding by Celltype",
-    "method_summary": "Cells are embedded as a one-hot encoding of celltype labels",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
-    "is_baseline": true,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_feature",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "celltype_random_embedding",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_embed/methods/baseline.py"
-  },
-  {
-    "method_name": "Random Graph by Celltype",
-    "method_summary": "Cells are embedded as a one-hot encoding of celltype labels. A graph is then built on this embedding",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
-    "is_baseline": true,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_feature",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "celltype_random_graph",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/baseline.py"
-  },
-  {
-    "method_name": "Random Integration by Celltype",
-    "method_summary": "Feature values, embedding coordinates, and graph connectivity are all randomly permuted within each celltype label",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
-    "is_baseline": true,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_feature",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "celltype_random_integration",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/_common/methods/baseline.py"
-  },
-  {
-    "method_name": "Combat (full/scaled)",
-    "method_summary": "ComBat uses an Empirical Bayes (EB) approach to correct for batch effects. It estimates batch-specific parameters by pooling information across genes in each batch and shrinks the estimates towards the overall mean of the batch effect estimates across all genes. These parameters are then used to adjust the data for batch effects, leading to more accurate and reproducible results.",
-    "paper_name": "Adjusting batch effects in microarray expression data using empirical Bayes methods",
-    "paper_reference": "hansen2012removing",
-    "paper_year": 2007,
-    "code_url": "https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.combat.html/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_feature",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "combat_full_scaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/combat.py"
-  },
-  {
-    "method_name": "Combat (full/unscaled)",
-    "method_summary": "ComBat uses an Empirical Bayes (EB) approach to correct for batch effects. It estimates batch-specific parameters by pooling information across genes in each batch and shrinks the estimates towards the overall mean of the batch effect estimates across all genes. These parameters are then used to adjust the data for batch effects, leading to more accurate and reproducible results.",
-    "paper_name": "Adjusting batch effects in microarray expression data using empirical Bayes methods",
-    "paper_reference": "hansen2012removing",
-    "paper_year": 2007,
-    "code_url": "https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.combat.html/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_feature",
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-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
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-  },
-  {
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-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
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-]
\ No newline at end of file
diff --git a/results/batch_integration_feature/data/metric_info.json b/results/batch_integration_feature/data/metric_info.json
deleted file mode 100644
index 353e0183..00000000
--- a/results/batch_integration_feature/data/metric_info.json
+++ /dev/null
@@ -1,134 +0,0 @@
-[
-  {
-    "metric_name": "ARI",
-    "metric_summary": "ARI (Adjusted Rand Index) compares the overlap of two clusterings. It considers both correct clustering overlaps while also counting correct disagreements between two clustering.",
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-  },
-  {
-    "metric_name": "Cell Cycle Score",
-    "metric_summary": "The cell-cycle conservation score evaluates how well the cell-cycle effect can be captured before and after integration.",
-    "paper_reference": "luecken2022benchmarking",
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-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "metric_id": "cc_score",
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-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "Graph connectivity",
-    "metric_summary": "The graph connectivity metric assesses whether the kNN graph representation, G, of the integrated data connects all cells with the same cell identity label.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "metric_id": "graph_connectivity",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/graph_connectivity.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "HVG conservation",
-    "metric_summary": "This metric computes the average percentage of overlapping highly variable genes per batch before and after integration.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_feature",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "metric_id": "hvg_conservation",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/hvg_conservation.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "Isolated label F1",
-    "metric_summary": "Isolated cell labels are identified as the labels present in the least number of batches in the integration task. The score evaluates how well these isolated labels separate from other cell identities based on clustering.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_feature",
-    "commit_sha": "ee7836251c4c6c371471e95eb7aa6a3e9f133b43",
-    "metric_id": "isolated_labels_f1",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/iso_label_f1.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "Isolated label Silhouette",
-    "metric_summary": "This score evaluates the compactness for the label(s) that is(are) shared by fewest batches. It indicates how well rare cell types can be preserved after integration.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_feature",
-    "commit_sha": "ee7836251c4c6c371471e95eb7aa6a3e9f133b43",
-    "metric_id": "isolated_labels_sil",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/iso_label_sil.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "kBET",
-    "metric_summary": "kBET determines whether the label composition of a k nearest neighborhood of a cell is similar to the expected (global) label composition. The test is repeated for a random subset of cells, and the results are summarized as a rejection rate over all tested neighborhoods.",
-    "paper_reference": "bttner2018test",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
-    "task_id": "batch_integration_feature",
-    "commit_sha": "ee7836251c4c6c371471e95eb7aa6a3e9f133b43",
-    "metric_id": "kBET",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/kBET.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "NMI",
-    "metric_summary": "NMI compares the overlap of two clusterings. We used NMI to compare the cell-type labels with Louvain clusters computed on the integrated dataset.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_feature",
-    "commit_sha": "ee7836251c4c6c371471e95eb7aa6a3e9f133b43",
-    "metric_id": "nmi",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/nmi.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "PC Regression",
-    "metric_summary": "This compares the explained variance by batch before and after integration. It returns a score between 0 and 1 (scaled=True) with 0 if the variance contribution hasn\u2019t changed. The larger the score, the more different the variance contributions are before and after integration.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_feature",
-    "commit_sha": "ee7836251c4c6c371471e95eb7aa6a3e9f133b43",
-    "metric_id": "pcr",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/pcr.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "Silhouette",
-    "metric_summary": "The absolute silhouette with is computed on cell identity labels, measuring their compactness.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_feature",
-    "commit_sha": "ee7836251c4c6c371471e95eb7aa6a3e9f133b43",
-    "metric_id": "silhouette",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/silhouette.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "Batch ASW",
-    "metric_summary": "The absolute silhouette width is computed over batch labels per cell. As 0 then indicates that batches are well mixed and any deviation from 0 indicates a batch effect, we use the 1-abs(ASW) to map the score to the scale [0;1].",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_feature",
-    "commit_sha": "ee7836251c4c6c371471e95eb7aa6a3e9f133b43",
-    "metric_id": "silhouette_batch",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/sil_batch.py",
-    "code_version": "v1.0.0"
-  }
-]
\ No newline at end of file
diff --git a/results/batch_integration_feature/data/quality_control.json b/results/batch_integration_feature/data/quality_control.json
deleted file mode 100644
index 0dc0990a..00000000
--- a/results/batch_integration_feature/data/quality_control.json
+++ /dev/null
@@ -1,5462 +0,0 @@
-[
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Task info", 
-        "name": "Pct 'task_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_id' should be defined\n  Task id: batch_integration_feature\n  Field: task_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Task info", 
-        "name": "Pct 'commit_sha' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'commit_sha' should be defined\n  Task id: batch_integration_feature\n  Field: commit_sha\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Task info", 
-        "name": "Pct 'task_name' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_name' should be defined\n  Task id: batch_integration_feature\n  Field: task_name\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Task info", 
-        "name": "Pct 'task_summary' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_summary' should be defined\n  Task id: batch_integration_feature\n  Field: task_summary\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Task info", 
-        "name": "Pct 'task_description' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_description' should be defined\n  Task id: batch_integration_feature\n  Field: task_description\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Method info", 
-        "name": "Pct 'task_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'task_id' should be defined\n  Task id: batch_integration_feature\n  Field: task_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Method info", 
-        "name": "Pct 'commit_sha' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'commit_sha' should be defined\n  Task id: batch_integration_feature\n  Field: commit_sha\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Method info", 
-        "name": "Pct 'method_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'method_id' should be defined\n  Task id: batch_integration_feature\n  Field: method_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Method info", 
-        "name": "Pct 'method_name' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'method_name' should be defined\n  Task id: batch_integration_feature\n  Field: method_name\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Method info", 
-        "name": "Pct 'method_summary' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'method_summary' should be defined\n  Task id: batch_integration_feature\n  Field: method_summary\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Method info", 
-        "name": "Pct 'paper_reference' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'paper_reference' should be defined\n  Task id: batch_integration_feature\n  Field: paper_reference\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Method info", 
-        "name": "Pct 'is_baseline' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'is_baseline' should be defined\n  Task id: batch_integration_feature\n  Field: is_baseline\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Metric info", 
-        "name": "Pct 'task_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'task_id' should be defined\n  Task id: batch_integration_feature\n  Field: task_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Metric info", 
-        "name": "Pct 'commit_sha' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'commit_sha' should be defined\n  Task id: batch_integration_feature\n  Field: commit_sha\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Metric info", 
-        "name": "Pct 'metric_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'metric_id' should be defined\n  Task id: batch_integration_feature\n  Field: metric_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Metric info", 
-        "name": "Pct 'metric_name' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'metric_name' should be defined\n  Task id: batch_integration_feature\n  Field: metric_name\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Metric info", 
-        "name": "Pct 'metric_summary' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'metric_summary' should be defined\n  Task id: batch_integration_feature\n  Field: metric_summary\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Metric info", 
-        "name": "Pct 'paper_reference' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'paper_reference' should be defined\n  Task id: batch_integration_feature\n  Field: paper_reference\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Metric info", 
-        "name": "Pct 'maximize' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'maximize' should be defined\n  Task id: batch_integration_feature\n  Field: maximize\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Dataset info", 
-        "name": "Pct 'task_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'task_id' should be defined\n  Task id: batch_integration_feature\n  Field: task_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Dataset info", 
-        "name": "Pct 'commit_sha' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'commit_sha' should be defined\n  Task id: batch_integration_feature\n  Field: commit_sha\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Dataset info", 
-        "name": "Pct 'dataset_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'dataset_id' should be defined\n  Task id: batch_integration_feature\n  Field: dataset_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Dataset info", 
-        "name": "Pct 'dataset_name' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'dataset_name' should be defined\n  Task id: batch_integration_feature\n  Field: dataset_name\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Dataset info", 
-        "name": "Pct 'dataset_summary' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'dataset_summary' should be defined\n  Task id: batch_integration_feature\n  Field: dataset_summary\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Dataset info", 
-        "name": "Pct 'data_reference' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'data_reference' should be defined\n  Task id: batch_integration_feature\n  Field: data_reference\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw data", 
-        "name": "Number of results", 
-        "value": 75, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "len(results) == len(method_info) * len(metric_info) * len(dataset_info)", 
-        "message": "Number of results should be equal to #methods × #metrics × #datasets.\n  Task id: batch_integration_feature\n  Number of results: 75\n  Number of methods: 22\n  Number of metrics: 11\n  Number of datasets: 3\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Metric 'ari' %missing", 
-        "value": -0.13636363636363646, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  Metric id: ari\n  Percentage missing: -14%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Metric 'cc_score' %missing", 
-        "value": -0.13636363636363646, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  Metric id: cc_score\n  Percentage missing: -14%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Metric 'graph_connectivity' %missing", 
-        "value": -0.13636363636363646, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  Metric id: graph_connectivity\n  Percentage missing: -14%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Metric 'hvg_conservation' %missing", 
-        "value": -0.13636363636363646, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  Metric id: hvg_conservation\n  Percentage missing: -14%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Metric 'isolated_labels_f1' %missing", 
-        "value": -0.13636363636363646, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  Metric id: isolated_labels_f1\n  Percentage missing: -14%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Metric 'isolated_labels_sil' %missing", 
-        "value": -0.13636363636363646, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  Metric id: isolated_labels_sil\n  Percentage missing: -14%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Metric 'kBET' %missing", 
-        "value": -0.13636363636363646, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  Metric id: kBET\n  Percentage missing: -14%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Metric 'nmi' %missing", 
-        "value": -0.13636363636363646, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  Metric id: nmi\n  Percentage missing: -14%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Metric 'pcr' %missing", 
-        "value": -0.13636363636363646, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  Metric id: pcr\n  Percentage missing: -14%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Metric 'silhouette' %missing", 
-        "value": -0.13636363636363646, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  Metric id: silhouette\n  Percentage missing: -14%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Metric 'silhouette_batch' %missing", 
-        "value": -0.13636363636363646, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  Metric id: silhouette_batch\n  Percentage missing: -14%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'batch_random_integration' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: batch_random_integration\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'celltype_random_integration' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: celltype_random_integration\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'combat_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: combat_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'combat_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: combat_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'combat_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: combat_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'combat_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: combat_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_feature_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: fastmnn_feature_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_feature_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: fastmnn_feature_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_feature_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: fastmnn_feature_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_feature_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: fastmnn_feature_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'mnn_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: mnn_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'mnn_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: mnn_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'mnn_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: mnn_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'mnn_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: mnn_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'no_integration' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: no_integration\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'random_integration' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: random_integration\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'scalex_full' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: scalex_full\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'scalex_hvg' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: scalex_hvg\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_feature_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: scanorama_feature_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_feature_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: scanorama_feature_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_feature_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: scanorama_feature_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_feature_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  method id: scanorama_feature_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Dataset 'immune_batch' %missing", 
-        "value": -0.13636363636363646, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  dataset id: immune_batch\n  Percentage missing: -14%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Dataset 'lung_batch' %missing", 
-        "value": -0.13636363636363646, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  dataset id: lung_batch\n  Percentage missing: -14%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Raw results", 
-        "name": "Dataset 'pancreas_batch' %missing", 
-        "value": -0.13636363636363646, 
-        "severity": 0, 
-        "severity_value": -1.3636363636363646, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_feature\n  dataset id: pancreas_batch\n  Percentage missing: -14%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration ari", 
-        "value": 0.011003980819852143, 
-        "severity": 0, 
-        "severity_value": -0.011003980819852143, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: ari\n  Worst score: 0.011003980819852143%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration ari", 
-        "value": 0.12257290320675532, 
-        "severity": 0, 
-        "severity_value": 0.06128645160337766, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: ari\n  Best score: 0.12257290320675532%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration ari", 
-        "value": 0.2189768423578631, 
-        "severity": 0, 
-        "severity_value": -0.2189768423578631, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: ari\n  Worst score: 0.2189768423578631%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration ari", 
-        "value": 0.3561208605516907, 
-        "severity": 0, 
-        "severity_value": 0.17806043027584534, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: ari\n  Best score: 0.3561208605516907%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled ari", 
-        "value": 0.5096241262215799, 
-        "severity": 0, 
-        "severity_value": -0.5096241262215799, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: ari\n  Worst score: 0.5096241262215799%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled ari", 
-        "value": 0.7268192775804326, 
-        "severity": 0, 
-        "severity_value": 0.3634096387902163, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: ari\n  Best score: 0.7268192775804326%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled ari", 
-        "value": 0.4678499008039894, 
-        "severity": 0, 
-        "severity_value": -0.4678499008039894, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: ari\n  Worst score: 0.4678499008039894%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled ari", 
-        "value": 0.7465216179372077, 
-        "severity": 0, 
-        "severity_value": 0.37326080896860386, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: ari\n  Best score: 0.7465216179372077%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled ari", 
-        "value": 0.46564702389879836, 
-        "severity": 0, 
-        "severity_value": -0.46564702389879836, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: ari\n  Worst score: 0.46564702389879836%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled ari", 
-        "value": 0.9475705661661228, 
-        "severity": 0, 
-        "severity_value": 0.4737852830830614, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: ari\n  Best score: 0.9475705661661228%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled ari", 
-        "value": 0.5502077984427347, 
-        "severity": 0, 
-        "severity_value": -0.5502077984427347, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.5502077984427347%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled ari", 
-        "value": 0.9442294456583428, 
-        "severity": 0, 
-        "severity_value": 0.4721147228291714, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: ari\n  Best score: 0.9442294456583428%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled ari", 
-        "value": 0.5037781661493705, 
-        "severity": 0, 
-        "severity_value": -0.5037781661493705, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: ari\n  Worst score: 0.5037781661493705%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled ari", 
-        "value": 0.8839228293601861, 
-        "severity": 0, 
-        "severity_value": 0.44196141468009303, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: ari\n  Best score: 0.8839228293601861%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled ari", 
-        "value": 0.49344373551293885, 
-        "severity": 0, 
-        "severity_value": -0.49344373551293885, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: ari\n  Worst score: 0.49344373551293885%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled ari", 
-        "value": 0.8842993328699347, 
-        "severity": 0, 
-        "severity_value": 0.44214966643496734, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: ari\n  Best score: 0.8842993328699347%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled ari", 
-        "value": 0.5562441230392319, 
-        "severity": 0, 
-        "severity_value": -0.5562441230392319, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: ari\n  Worst score: 0.5562441230392319%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled ari", 
-        "value": 0.9264153816595343, 
-        "severity": 0, 
-        "severity_value": 0.46320769082976715, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: ari\n  Best score: 0.9264153816595343%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled ari", 
-        "value": 0.5539682483281614, 
-        "severity": 0, 
-        "severity_value": -0.5539682483281614, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.5539682483281614%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled ari", 
-        "value": 0.841080352172894, 
-        "severity": 0, 
-        "severity_value": 0.420540176086447, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: ari\n  Best score: 0.841080352172894%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled ari", 
-        "value": 0.47466142685871804, 
-        "severity": 0, 
-        "severity_value": -0.47466142685871804, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: ari\n  Worst score: 0.47466142685871804%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled ari", 
-        "value": 0.6946798464813778, 
-        "severity": 0, 
-        "severity_value": 0.3473399232406889, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: ari\n  Best score: 0.6946798464813778%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled ari", 
-        "value": 0.3527989860419826, 
-        "severity": 0, 
-        "severity_value": -0.3527989860419826, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: ari\n  Worst score: 0.3527989860419826%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled ari", 
-        "value": 0.501348527763972, 
-        "severity": 0, 
-        "severity_value": 0.250674263881986, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: ari\n  Best score: 0.501348527763972%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled ari", 
-        "value": 0.5595533635492874, 
-        "severity": 0, 
-        "severity_value": -0.5595533635492874, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: ari\n  Worst score: 0.5595533635492874%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled ari", 
-        "value": 0.9454186884494393, 
-        "severity": 0, 
-        "severity_value": 0.47270934422471966, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: ari\n  Best score: 0.9454186884494393%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled ari", 
-        "value": 0.49951385779117513, 
-        "severity": 0, 
-        "severity_value": -0.49951385779117513, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.49951385779117513%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled ari", 
-        "value": 0.8066354485086064, 
-        "severity": 0, 
-        "severity_value": 0.4033177242543032, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: ari\n  Best score: 0.8066354485086064%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration ari", 
-        "value": 0.2189501869349401, 
-        "severity": 0, 
-        "severity_value": -0.2189501869349401, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: ari\n  Worst score: 0.2189501869349401%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score no_integration ari", 
-        "value": 0.3834709266669674, 
-        "severity": 0, 
-        "severity_value": 0.1917354633334837, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: ari\n  Best score: 0.3834709266669674%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration ari", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: ari\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score random_integration ari", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: ari\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full ari", 
-        "value": 0.5454128026387511, 
-        "severity": 0, 
-        "severity_value": -0.5454128026387511, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: ari\n  Worst score: 0.5454128026387511%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full ari", 
-        "value": 0.898815493393237, 
-        "severity": 0, 
-        "severity_value": 0.4494077466966185, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: ari\n  Best score: 0.898815493393237%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg ari", 
-        "value": 0.537028812578972, 
-        "severity": 0, 
-        "severity_value": -0.537028812578972, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: ari\n  Worst score: 0.537028812578972%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg ari", 
-        "value": 0.9280317592201297, 
-        "severity": 0, 
-        "severity_value": 0.46401587961006485, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: ari\n  Best score: 0.9280317592201297%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled ari", 
-        "value": 0.44403712373673526, 
-        "severity": 0, 
-        "severity_value": -0.44403712373673526, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: ari\n  Worst score: 0.44403712373673526%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled ari", 
-        "value": 0.6682316219890497, 
-        "severity": 0, 
-        "severity_value": 0.33411581099452486, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: ari\n  Best score: 0.6682316219890497%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled ari", 
-        "value": 0.4274074840615586, 
-        "severity": 0, 
-        "severity_value": -0.4274074840615586, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: ari\n  Worst score: 0.4274074840615586%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled ari", 
-        "value": 0.4635966451639077, 
-        "severity": 0, 
-        "severity_value": 0.23179832258195385, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: ari\n  Best score: 0.4635966451639077%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled ari", 
-        "value": 0.4579230588791482, 
-        "severity": 0, 
-        "severity_value": -0.4579230588791482, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: ari\n  Worst score: 0.4579230588791482%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled ari", 
-        "value": 0.9422598939985116, 
-        "severity": 0, 
-        "severity_value": 0.4711299469992558, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: ari\n  Best score: 0.9422598939985116%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled ari", 
-        "value": 0.48204504833501427, 
-        "severity": 0, 
-        "severity_value": -0.48204504833501427, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.48204504833501427%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled ari", 
-        "value": 0.7433403173409046, 
-        "severity": 0, 
-        "severity_value": 0.3716701586704523, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: ari\n  Best score: 0.7433403173409046%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration cc_score", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: cc_score\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration cc_score", 
-        "value": 0.013937826888960402, 
-        "severity": 0, 
-        "severity_value": 0.006968913444480201, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: cc_score\n  Best score: 0.013937826888960402%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration cc_score", 
-        "value": 0.16932943955210417, 
-        "severity": 0, 
-        "severity_value": -0.16932943955210417, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: cc_score\n  Worst score: 0.16932943955210417%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration cc_score", 
-        "value": 0.4913535237243391, 
-        "severity": 0, 
-        "severity_value": 0.24567676186216955, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: cc_score\n  Best score: 0.4913535237243391%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled cc_score", 
-        "value": 0.4223770483200592, 
-        "severity": 0, 
-        "severity_value": -0.4223770483200592, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: cc_score\n  Worst score: 0.4223770483200592%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled cc_score", 
-        "value": 0.7969030746295956, 
-        "severity": 0, 
-        "severity_value": 0.3984515373147978, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: cc_score\n  Best score: 0.7969030746295956%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled cc_score", 
-        "value": 0.76548023581928, 
-        "severity": 0, 
-        "severity_value": -0.76548023581928, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: cc_score\n  Worst score: 0.76548023581928%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled cc_score", 
-        "value": 0.8593743973860972, 
-        "severity": 0, 
-        "severity_value": 0.4296871986930486, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: cc_score\n  Best score: 0.8593743973860972%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled cc_score", 
-        "value": 0.5825775946239846, 
-        "severity": 0, 
-        "severity_value": -0.5825775946239846, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: cc_score\n  Worst score: 0.5825775946239846%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled cc_score", 
-        "value": 0.770252338920565, 
-        "severity": 0, 
-        "severity_value": 0.3851261694602825, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: cc_score\n  Best score: 0.770252338920565%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled cc_score", 
-        "value": 0.7589108624258526, 
-        "severity": 0, 
-        "severity_value": -0.7589108624258526, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: cc_score\n  Worst score: 0.7589108624258526%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled cc_score", 
-        "value": 0.8659016355060156, 
-        "severity": 0, 
-        "severity_value": 0.4329508177530078, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: cc_score\n  Best score: 0.8659016355060156%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled cc_score", 
-        "value": 0.3464187104644399, 
-        "severity": 0, 
-        "severity_value": -0.3464187104644399, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: cc_score\n  Worst score: 0.3464187104644399%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled cc_score", 
-        "value": 0.9073447374676129, 
-        "severity": 0, 
-        "severity_value": 0.45367236873380645, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: cc_score\n  Best score: 0.9073447374676129%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled cc_score", 
-        "value": 0.3464218524012018, 
-        "severity": 0, 
-        "severity_value": -0.3464218524012018, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: cc_score\n  Worst score: 0.3464218524012018%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled cc_score", 
-        "value": 0.9072981672523214, 
-        "severity": 0, 
-        "severity_value": 0.4536490836261607, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: cc_score\n  Best score: 0.9072981672523214%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled cc_score", 
-        "value": 0.6499843191376706, 
-        "severity": 0, 
-        "severity_value": -0.6499843191376706, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: cc_score\n  Worst score: 0.6499843191376706%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled cc_score", 
-        "value": 0.8151110817952885, 
-        "severity": 0, 
-        "severity_value": 0.40755554089764423, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: cc_score\n  Best score: 0.8151110817952885%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled cc_score", 
-        "value": 0.6499857783672051, 
-        "severity": 0, 
-        "severity_value": -0.6499857783672051, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: cc_score\n  Worst score: 0.6499857783672051%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled cc_score", 
-        "value": 0.8150982681781299, 
-        "severity": 0, 
-        "severity_value": 0.40754913408906496, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: cc_score\n  Best score: 0.8150982681781299%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled cc_score", 
-        "value": 0.3660897310715203, 
-        "severity": 0, 
-        "severity_value": -0.3660897310715203, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: cc_score\n  Worst score: 0.3660897310715203%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled cc_score", 
-        "value": 0.7711415051749245, 
-        "severity": 0, 
-        "severity_value": 0.38557075258746226, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: cc_score\n  Best score: 0.7711415051749245%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled cc_score", 
-        "value": 0.6500836662833465, 
-        "severity": 0, 
-        "severity_value": -0.6500836662833465, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: cc_score\n  Worst score: 0.6500836662833465%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled cc_score", 
-        "value": 0.8413257857490386, 
-        "severity": 0, 
-        "severity_value": 0.4206628928745193, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: cc_score\n  Best score: 0.8413257857490386%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled cc_score", 
-        "value": 0.5493172059800754, 
-        "severity": 0, 
-        "severity_value": -0.5493172059800754, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: cc_score\n  Worst score: 0.5493172059800754%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled cc_score", 
-        "value": 0.9010530245326487, 
-        "severity": 0, 
-        "severity_value": 0.45052651226632434, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: cc_score\n  Best score: 0.9010530245326487%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled cc_score", 
-        "value": 0.7471414689193315, 
-        "severity": 0, 
-        "severity_value": -0.7471414689193315, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: cc_score\n  Worst score: 0.7471414689193315%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled cc_score", 
-        "value": 0.8473925203645843, 
-        "severity": 0, 
-        "severity_value": 0.4236962601822922, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: cc_score\n  Best score: 0.8473925203645843%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration cc_score", 
-        "value": 0.6610834183909216, 
-        "severity": 0, 
-        "severity_value": -0.6610834183909216, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: cc_score\n  Worst score: 0.6610834183909216%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score no_integration cc_score", 
-        "value": 0.7467149011903403, 
-        "severity": 0, 
-        "severity_value": 0.37335745059517017, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: cc_score\n  Best score: 0.7467149011903403%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration cc_score", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: cc_score\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score random_integration cc_score", 
-        "value": 0.04193781530290718, 
-        "severity": 0, 
-        "severity_value": 0.02096890765145359, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: cc_score\n  Best score: 0.04193781530290718%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full cc_score", 
-        "value": 0.4818274710506096, 
-        "severity": 0, 
-        "severity_value": -0.4818274710506096, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: cc_score\n  Worst score: 0.4818274710506096%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full cc_score", 
-        "value": 0.620279435471904, 
-        "severity": 0, 
-        "severity_value": 0.310139717735952, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: cc_score\n  Best score: 0.620279435471904%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg cc_score", 
-        "value": 0.6065010210767469, 
-        "severity": 0, 
-        "severity_value": -0.6065010210767469, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: cc_score\n  Worst score: 0.6065010210767469%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg cc_score", 
-        "value": 0.7095925338770973, 
-        "severity": 0, 
-        "severity_value": 0.35479626693854865, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: cc_score\n  Best score: 0.7095925338770973%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled cc_score", 
-        "value": 0.04341134995780015, 
-        "severity": 0, 
-        "severity_value": -0.04341134995780015, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: cc_score\n  Worst score: 0.04341134995780015%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled cc_score", 
-        "value": 0.4557348772862119, 
-        "severity": 0, 
-        "severity_value": 0.22786743864310596, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: cc_score\n  Best score: 0.4557348772862119%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled cc_score", 
-        "value": 0.07436286726340889, 
-        "severity": 0, 
-        "severity_value": -0.07436286726340889, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: cc_score\n  Worst score: 0.07436286726340889%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled cc_score", 
-        "value": 0.38655543731511577, 
-        "severity": 0, 
-        "severity_value": 0.19327771865755788, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: cc_score\n  Best score: 0.38655543731511577%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled cc_score", 
-        "value": 0.07041750858438396, 
-        "severity": 0, 
-        "severity_value": -0.07041750858438396, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: cc_score\n  Worst score: 0.07041750858438396%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled cc_score", 
-        "value": 0.4259278392346852, 
-        "severity": 0, 
-        "severity_value": 0.2129639196173426, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: cc_score\n  Best score: 0.4259278392346852%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled cc_score", 
-        "value": 0.07382142930944877, 
-        "severity": 0, 
-        "severity_value": -0.07382142930944877, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: cc_score\n  Worst score: 0.07382142930944877%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled cc_score", 
-        "value": 0.37962793294850344, 
-        "severity": 0, 
-        "severity_value": 0.18981396647425172, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: cc_score\n  Best score: 0.37962793294850344%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration graph_connectivity", 
-        "value": 0.13684979525313318, 
-        "severity": 0, 
-        "severity_value": -0.13684979525313318, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: graph_connectivity\n  Worst score: 0.13684979525313318%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration graph_connectivity", 
-        "value": 0.4383453144923638, 
-        "severity": 0, 
-        "severity_value": 0.2191726572461819, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: graph_connectivity\n  Best score: 0.4383453144923638%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration graph_connectivity", 
-        "value": 0.7751399364056503, 
-        "severity": 0, 
-        "severity_value": -0.7751399364056503, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: graph_connectivity\n  Worst score: 0.7751399364056503%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration graph_connectivity", 
-        "value": 0.8091419482349493, 
-        "severity": 0, 
-        "severity_value": 0.40457097411747467, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: graph_connectivity\n  Best score: 0.8091419482349493%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled graph_connectivity", 
-        "value": 0.9307488395155908, 
-        "severity": 0, 
-        "severity_value": -0.9307488395155908, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9307488395155908%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled graph_connectivity", 
-        "value": 0.9925702701638219, 
-        "severity": 0, 
-        "severity_value": 0.49628513508191097, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9925702701638219%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled graph_connectivity", 
-        "value": 0.9363924963774355, 
-        "severity": 0, 
-        "severity_value": -0.9363924963774355, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9363924963774355%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled graph_connectivity", 
-        "value": 0.9927008519821716, 
-        "severity": 0, 
-        "severity_value": 0.4963504259910858, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9927008519821716%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled graph_connectivity", 
-        "value": 0.9287554433738452, 
-        "severity": 0, 
-        "severity_value": -0.9287554433738452, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9287554433738452%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled graph_connectivity", 
-        "value": 0.9944488801121246, 
-        "severity": 0, 
-        "severity_value": 0.4972244400560623, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9944488801121246%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled graph_connectivity", 
-        "value": 0.9470651029775061, 
-        "severity": 0, 
-        "severity_value": -0.9470651029775061, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9470651029775061%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled graph_connectivity", 
-        "value": 0.9941909474715658, 
-        "severity": 0, 
-        "severity_value": 0.4970954737357829, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9941909474715658%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled graph_connectivity", 
-        "value": 0.9466708548550004, 
-        "severity": 0, 
-        "severity_value": -0.9466708548550004, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9466708548550004%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled graph_connectivity", 
-        "value": 0.9720736645592167, 
-        "severity": 0, 
-        "severity_value": 0.48603683227960837, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9720736645592167%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled graph_connectivity", 
-        "value": 0.9449561070189656, 
-        "severity": 0, 
-        "severity_value": -0.9449561070189656, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9449561070189656%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled graph_connectivity", 
-        "value": 0.9715822703689649, 
-        "severity": 0, 
-        "severity_value": 0.48579113518448247, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9715822703689649%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled graph_connectivity", 
-        "value": 0.9431612530306095, 
-        "severity": 0, 
-        "severity_value": -0.9431612530306095, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9431612530306095%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled graph_connectivity", 
-        "value": 0.9739211878947122, 
-        "severity": 0, 
-        "severity_value": 0.4869605939473561, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9739211878947122%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled graph_connectivity", 
-        "value": 0.9465331185787045, 
-        "severity": 0, 
-        "severity_value": -0.9465331185787045, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9465331185787045%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled graph_connectivity", 
-        "value": 0.9728892642915491, 
-        "severity": 0, 
-        "severity_value": 0.48644463214577455, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9728892642915491%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled graph_connectivity", 
-        "value": 0.9485101848711353, 
-        "severity": 0, 
-        "severity_value": -0.9485101848711353, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9485101848711353%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled graph_connectivity", 
-        "value": 0.9930981809387608, 
-        "severity": 0, 
-        "severity_value": 0.4965490904693804, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9930981809387608%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled graph_connectivity", 
-        "value": 0.9366791027024343, 
-        "severity": 0, 
-        "severity_value": -0.9366791027024343, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9366791027024343%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled graph_connectivity", 
-        "value": 0.9937499172558295, 
-        "severity": 0, 
-        "severity_value": 0.49687495862791475, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9937499172558295%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled graph_connectivity", 
-        "value": 0.9719573692792381, 
-        "severity": 0, 
-        "severity_value": -0.9719573692792381, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9719573692792381%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled graph_connectivity", 
-        "value": 0.9935415765479527, 
-        "severity": 0, 
-        "severity_value": 0.49677078827397636, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9935415765479527%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled graph_connectivity", 
-        "value": 0.9877944703549977, 
-        "severity": 0, 
-        "severity_value": -0.9877944703549977, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9877944703549977%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled graph_connectivity", 
-        "value": 0.9945539242349454, 
-        "severity": 0, 
-        "severity_value": 0.4972769621174727, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9945539242349454%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration graph_connectivity", 
-        "value": 0.7751399364056503, 
-        "severity": 0, 
-        "severity_value": -0.7751399364056503, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: graph_connectivity\n  Worst score: 0.7751399364056503%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score no_integration graph_connectivity", 
-        "value": 0.8091419482349493, 
-        "severity": 0, 
-        "severity_value": 0.40457097411747467, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: graph_connectivity\n  Best score: 0.8091419482349493%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration graph_connectivity", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: graph_connectivity\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score random_integration graph_connectivity", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: graph_connectivity\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full graph_connectivity", 
-        "value": 0.9468808795963009, 
-        "severity": 0, 
-        "severity_value": -0.9468808795963009, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: graph_connectivity\n  Worst score: 0.9468808795963009%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full graph_connectivity", 
-        "value": 0.9743835466894744, 
-        "severity": 0, 
-        "severity_value": 0.4871917733447372, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: graph_connectivity\n  Best score: 0.9743835466894744%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg graph_connectivity", 
-        "value": 0.963176990713195, 
-        "severity": 0, 
-        "severity_value": -0.963176990713195, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: graph_connectivity\n  Worst score: 0.963176990713195%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg graph_connectivity", 
-        "value": 0.9847160249930949, 
-        "severity": 0, 
-        "severity_value": 0.4923580124965474, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: graph_connectivity\n  Best score: 0.9847160249930949%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled graph_connectivity", 
-        "value": 0.7674108177741729, 
-        "severity": 0, 
-        "severity_value": -0.7674108177741729, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.7674108177741729%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled graph_connectivity", 
-        "value": 0.9802513594444672, 
-        "severity": 0, 
-        "severity_value": 0.4901256797222336, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9802513594444672%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled graph_connectivity", 
-        "value": 0.7646546288686339, 
-        "severity": 0, 
-        "severity_value": -0.7646546288686339, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.7646546288686339%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled graph_connectivity", 
-        "value": 0.9072234045489983, 
-        "severity": 0, 
-        "severity_value": 0.45361170227449915, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9072234045489983%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled graph_connectivity", 
-        "value": 0.8431649081399656, 
-        "severity": 0, 
-        "severity_value": -0.8431649081399656, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.8431649081399656%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled graph_connectivity", 
-        "value": 0.9851664297877089, 
-        "severity": 0, 
-        "severity_value": 0.4925832148938544, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9851664297877089%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled graph_connectivity", 
-        "value": 0.8486493496731864, 
-        "severity": 0, 
-        "severity_value": -0.8486493496731864, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.8486493496731864%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled graph_connectivity", 
-        "value": 0.9840860531897198, 
-        "severity": 0, 
-        "severity_value": 0.4920430265948599, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9840860531897198%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration hvg_conservation", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: hvg_conservation\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration hvg_conservation", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: hvg_conservation\n  Best score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration hvg_conservation", 
-        "value": 0.06723755153821773, 
-        "severity": 0, 
-        "severity_value": -0.06723755153821773, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: hvg_conservation\n  Worst score: 0.06723755153821773%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration hvg_conservation", 
-        "value": 0.2279465370595384, 
-        "severity": 0, 
-        "severity_value": 0.1139732685297692, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: hvg_conservation\n  Best score: 0.2279465370595384%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled hvg_conservation", 
-        "value": -0.7902023429179982, 
-        "severity": 0, 
-        "severity_value": 0.7902023429179982, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: hvg_conservation\n  Worst score: -0.7902023429179982%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled hvg_conservation", 
-        "value": -0.18775769108785276, 
-        "severity": 0, 
-        "severity_value": -0.09387884554392638, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: hvg_conservation\n  Best score: -0.18775769108785276%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled hvg_conservation", 
-        "value": 0.025759416767922202, 
-        "severity": 0, 
-        "severity_value": -0.025759416767922202, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: hvg_conservation\n  Worst score: 0.025759416767922202%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled hvg_conservation", 
-        "value": 0.08087535680304478, 
-        "severity": 0, 
-        "severity_value": 0.04043767840152239, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: hvg_conservation\n  Best score: 0.08087535680304478%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled hvg_conservation", 
-        "value": -0.784345047923323, 
-        "severity": 0, 
-        "severity_value": 0.784345047923323, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: hvg_conservation\n  Worst score: -0.784345047923323%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled hvg_conservation", 
-        "value": -0.18331747542023474, 
-        "severity": 0, 
-        "severity_value": -0.09165873771011737, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: hvg_conservation\n  Best score: -0.18331747542023474%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled hvg_conservation", 
-        "value": 0.03134872417983, 
-        "severity": 0, 
-        "severity_value": -0.03134872417983, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: hvg_conservation\n  Worst score: 0.03134872417983%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled hvg_conservation", 
-        "value": 0.08753568030447205, 
-        "severity": 0, 
-        "severity_value": 0.043767840152236025, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: hvg_conservation\n  Best score: 0.08753568030447205%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled hvg_conservation", 
-        "value": -0.6964856230031953, 
-        "severity": 0, 
-        "severity_value": 0.6964856230031953, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: hvg_conservation\n  Worst score: -0.6964856230031953%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled hvg_conservation", 
-        "value": -0.01966381224230877, 
-        "severity": 0, 
-        "severity_value": -0.009831906121154385, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: hvg_conservation\n  Best score: -0.01966381224230877%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled hvg_conservation", 
-        "value": -0.695953141640043, 
-        "severity": 0, 
-        "severity_value": 0.695953141640043, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: hvg_conservation\n  Worst score: -0.695953141640043%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled hvg_conservation", 
-        "value": -0.019029495718363303, 
-        "severity": 0, 
-        "severity_value": -0.009514747859181652, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: hvg_conservation\n  Best score: -0.019029495718363303%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled hvg_conservation", 
-        "value": -0.6719914802981901, 
-        "severity": 0, 
-        "severity_value": 0.6719914802981901, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: hvg_conservation\n  Worst score: -0.6719914802981901%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled hvg_conservation", 
-        "value": -0.10497938471297173, 
-        "severity": 0, 
-        "severity_value": -0.052489692356485866, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: hvg_conservation\n  Best score: -0.10497938471297173%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled hvg_conservation", 
-        "value": -0.6719914802981901, 
-        "severity": 0, 
-        "severity_value": 0.6719914802981901, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: hvg_conservation\n  Worst score: -0.6719914802981901%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled hvg_conservation", 
-        "value": -0.10497938471297173, 
-        "severity": 0, 
-        "severity_value": -0.052489692356485866, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: hvg_conservation\n  Best score: -0.10497938471297173%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled hvg_conservation", 
-        "value": -0.7742279020234295, 
-        "severity": 0, 
-        "severity_value": 0.7742279020234295, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: hvg_conservation\n  Worst score: -0.7742279020234295%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled hvg_conservation", 
-        "value": -0.15857913098636214, 
-        "severity": 0, 
-        "severity_value": -0.07928956549318107, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: hvg_conservation\n  Best score: -0.15857913098636214%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled hvg_conservation", 
-        "value": -0.264110756123536, 
-        "severity": 0, 
-        "severity_value": 0.264110756123536, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: hvg_conservation\n  Worst score: -0.264110756123536%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled hvg_conservation", 
-        "value": 0.2052013954963527, 
-        "severity": 0, 
-        "severity_value": 0.10260069774817634, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: hvg_conservation\n  Best score: 0.2052013954963527%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled hvg_conservation", 
-        "value": -0.6986155484558045, 
-        "severity": 0, 
-        "severity_value": 0.6986155484558045, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: hvg_conservation\n  Worst score: -0.6986155484558045%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled hvg_conservation", 
-        "value": -0.1160799238820171, 
-        "severity": 0, 
-        "severity_value": -0.05803996194100855, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: hvg_conservation\n  Best score: -0.1160799238820171%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled hvg_conservation", 
-        "value": -0.27369542066027724, 
-        "severity": 0, 
-        "severity_value": 0.27369542066027724, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: hvg_conservation\n  Worst score: -0.27369542066027724%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled hvg_conservation", 
-        "value": 0.09292737075800839, 
-        "severity": 0, 
-        "severity_value": 0.046463685379004194, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: hvg_conservation\n  Best score: 0.09292737075800839%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration hvg_conservation", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: hvg_conservation\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score no_integration hvg_conservation", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: hvg_conservation\n  Best score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration hvg_conservation", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: hvg_conservation\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score random_integration hvg_conservation", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: hvg_conservation\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full hvg_conservation", 
-        "value": -0.4030883919062835, 
-        "severity": 0, 
-        "severity_value": 0.4030883919062835, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: hvg_conservation\n  Worst score: -0.4030883919062835%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full hvg_conservation", 
-        "value": 0.1820488423723438, 
-        "severity": 0, 
-        "severity_value": 0.0910244211861719, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: hvg_conservation\n  Best score: 0.1820488423723438%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg hvg_conservation", 
-        "value": -0.18104366347177892, 
-        "severity": 0, 
-        "severity_value": 0.18104366347177892, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: hvg_conservation\n  Worst score: -0.18104366347177892%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg hvg_conservation", 
-        "value": 0.27592768791627026, 
-        "severity": 0, 
-        "severity_value": 0.13796384395813513, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: hvg_conservation\n  Best score: 0.27592768791627026%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled hvg_conservation", 
-        "value": -0.7039403620873274, 
-        "severity": 0, 
-        "severity_value": 0.7039403620873274, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: hvg_conservation\n  Worst score: -0.7039403620873274%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled hvg_conservation", 
-        "value": -0.18046305106248017, 
-        "severity": 0, 
-        "severity_value": -0.09023152553124009, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: hvg_conservation\n  Best score: -0.18046305106248017%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled hvg_conservation", 
-        "value": -0.5670926517571889, 
-        "severity": 0, 
-        "severity_value": 0.5670926517571889, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: hvg_conservation\n  Worst score: -0.5670926517571889%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled hvg_conservation", 
-        "value": -0.20742150333016166, 
-        "severity": 0, 
-        "severity_value": -0.10371075166508083, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: hvg_conservation\n  Best score: -0.20742150333016166%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled hvg_conservation", 
-        "value": -0.7541317790404286, 
-        "severity": 0, 
-        "severity_value": 0.7541317790404286, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: hvg_conservation\n  Worst score: -0.7541317790404286%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled hvg_conservation", 
-        "value": -0.17285125277513466, 
-        "severity": 0, 
-        "severity_value": -0.08642562638756733, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: hvg_conservation\n  Best score: -0.17285125277513466%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled hvg_conservation", 
-        "value": -0.7983362348784864, 
-        "severity": 0, 
-        "severity_value": 0.7983362348784864, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: hvg_conservation\n  Worst score: -0.7983362348784864%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled hvg_conservation", 
-        "value": -0.1918807484934981, 
-        "severity": 0, 
-        "severity_value": -0.09594037424674905, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: hvg_conservation\n  Best score: -0.1918807484934981%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration isolated_labels_f1", 
-        "value": 0.03751680200756959, 
-        "severity": 0, 
-        "severity_value": -0.03751680200756959, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: isolated_labels_f1\n  Worst score: 0.03751680200756959%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration isolated_labels_f1", 
-        "value": 0.09770823169379186, 
-        "severity": 0, 
-        "severity_value": 0.04885411584689593, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: isolated_labels_f1\n  Best score: 0.09770823169379186%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration isolated_labels_f1", 
-        "value": 0.7097050339009523, 
-        "severity": 0, 
-        "severity_value": -0.7097050339009523, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: isolated_labels_f1\n  Worst score: 0.7097050339009523%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration isolated_labels_f1", 
-        "value": 0.7890535603501757, 
-        "severity": 0, 
-        "severity_value": 0.39452678017508785, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: isolated_labels_f1\n  Best score: 0.7890535603501757%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled isolated_labels_f1", 
-        "value": 0.7861780441482044, 
-        "severity": 0, 
-        "severity_value": -0.7861780441482044, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7861780441482044%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled isolated_labels_f1", 
-        "value": 0.9484476372804961, 
-        "severity": 0, 
-        "severity_value": 0.47422381864024804, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9484476372804961%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled isolated_labels_f1", 
-        "value": 0.7045035422144319, 
-        "severity": 0, 
-        "severity_value": -0.7045035422144319, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7045035422144319%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled isolated_labels_f1", 
-        "value": 0.8502301450234127, 
-        "severity": 0, 
-        "severity_value": 0.4251150725117063, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8502301450234127%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled isolated_labels_f1", 
-        "value": 0.7002864911335213, 
-        "severity": 0, 
-        "severity_value": -0.7002864911335213, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7002864911335213%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled isolated_labels_f1", 
-        "value": 0.9552200942536132, 
-        "severity": 0, 
-        "severity_value": 0.4776100471268066, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9552200942536132%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled isolated_labels_f1", 
-        "value": 0.716346202278083, 
-        "severity": 0, 
-        "severity_value": -0.716346202278083, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.716346202278083%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled isolated_labels_f1", 
-        "value": 0.923893186364932, 
-        "severity": 0, 
-        "severity_value": 0.461946593182466, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.923893186364932%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled isolated_labels_f1", 
-        "value": 0.6298832245918136, 
-        "severity": 0, 
-        "severity_value": -0.6298832245918136, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.6298832245918136%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled isolated_labels_f1", 
-        "value": 0.7439015977399014, 
-        "severity": 0, 
-        "severity_value": 0.3719507988699507, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.7439015977399014%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled isolated_labels_f1", 
-        "value": 0.6320197221069034, 
-        "severity": 0, 
-        "severity_value": -0.6320197221069034, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.6320197221069034%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled isolated_labels_f1", 
-        "value": 0.7923964293682966, 
-        "severity": 0, 
-        "severity_value": 0.3961982146841483, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.7923964293682966%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled isolated_labels_f1", 
-        "value": 0.6616336846609389, 
-        "severity": 0, 
-        "severity_value": -0.6616336846609389, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.6616336846609389%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled isolated_labels_f1", 
-        "value": 0.8434232286128296, 
-        "severity": 0, 
-        "severity_value": 0.4217116143064148, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8434232286128296%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled isolated_labels_f1", 
-        "value": 0.7242069380126577, 
-        "severity": 0, 
-        "severity_value": -0.7242069380126577, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7242069380126577%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled isolated_labels_f1", 
-        "value": 0.8447268232007824, 
-        "severity": 0, 
-        "severity_value": 0.4223634116003912, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8447268232007824%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled isolated_labels_f1", 
-        "value": 0.8402518430007101, 
-        "severity": 0, 
-        "severity_value": -0.8402518430007101, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8402518430007101%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled isolated_labels_f1", 
-        "value": 0.9407309775151117, 
-        "severity": 0, 
-        "severity_value": 0.47036548875755585, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9407309775151117%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled isolated_labels_f1", 
-        "value": 0.847224352792382, 
-        "severity": 0, 
-        "severity_value": -0.847224352792382, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.847224352792382%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled isolated_labels_f1", 
-        "value": 0.8848453227936162, 
-        "severity": 0, 
-        "severity_value": 0.4424226613968081, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8848453227936162%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled isolated_labels_f1", 
-        "value": 0.794101542751482, 
-        "severity": 0, 
-        "severity_value": -0.794101542751482, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.794101542751482%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled isolated_labels_f1", 
-        "value": 0.9529080738976624, 
-        "severity": 0, 
-        "severity_value": 0.4764540369488312, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9529080738976624%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled isolated_labels_f1", 
-        "value": 0.725106029909262, 
-        "severity": 0, 
-        "severity_value": -0.725106029909262, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.725106029909262%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled isolated_labels_f1", 
-        "value": 0.9276191079002216, 
-        "severity": 0, 
-        "severity_value": 0.4638095539501108, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9276191079002216%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration isolated_labels_f1", 
-        "value": 0.6954721525479699, 
-        "severity": 0, 
-        "severity_value": -0.6954721525479699, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: isolated_labels_f1\n  Worst score: 0.6954721525479699%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score no_integration isolated_labels_f1", 
-        "value": 0.7800117179366748, 
-        "severity": 0, 
-        "severity_value": 0.3900058589683374, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: isolated_labels_f1\n  Best score: 0.7800117179366748%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration isolated_labels_f1", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: isolated_labels_f1\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score random_integration isolated_labels_f1", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: isolated_labels_f1\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full isolated_labels_f1", 
-        "value": 0.40959139246436754, 
-        "severity": 0, 
-        "severity_value": -0.40959139246436754, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: isolated_labels_f1\n  Worst score: 0.40959139246436754%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full isolated_labels_f1", 
-        "value": 0.7369154242654748, 
-        "severity": 0, 
-        "severity_value": 0.3684577121327374, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: isolated_labels_f1\n  Best score: 0.7369154242654748%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg isolated_labels_f1", 
-        "value": 0.6015467194276537, 
-        "severity": 0, 
-        "severity_value": -0.6015467194276537, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: isolated_labels_f1\n  Worst score: 0.6015467194276537%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg isolated_labels_f1", 
-        "value": 0.690113593112518, 
-        "severity": 0, 
-        "severity_value": 0.345056796556259, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: isolated_labels_f1\n  Best score: 0.690113593112518%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled isolated_labels_f1", 
-        "value": 0.8173110965535622, 
-        "severity": 0, 
-        "severity_value": -0.8173110965535622, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8173110965535622%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled isolated_labels_f1", 
-        "value": 0.9250289851326589, 
-        "severity": 0, 
-        "severity_value": 0.4625144925663294, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9250289851326589%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled isolated_labels_f1", 
-        "value": 0.7324463917568217, 
-        "severity": 0, 
-        "severity_value": -0.7324463917568217, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7324463917568217%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled isolated_labels_f1", 
-        "value": 0.8561797687576279, 
-        "severity": 0, 
-        "severity_value": 0.42808988437881396, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8561797687576279%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled isolated_labels_f1", 
-        "value": 0.8207851922815649, 
-        "severity": 0, 
-        "severity_value": -0.8207851922815649, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8207851922815649%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled isolated_labels_f1", 
-        "value": 0.9386235942533596, 
-        "severity": 0, 
-        "severity_value": 0.4693117971266798, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9386235942533596%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled isolated_labels_f1", 
-        "value": 0.8305768183068796, 
-        "severity": 0, 
-        "severity_value": -0.8305768183068796, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8305768183068796%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled isolated_labels_f1", 
-        "value": 0.9164630616432352, 
-        "severity": 0, 
-        "severity_value": 0.4582315308216176, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9164630616432352%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration isolated_labels_sil", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: isolated_labels_sil\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration isolated_labels_sil", 
-        "value": 0.12559626128378212, 
-        "severity": 0, 
-        "severity_value": 0.06279813064189106, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: isolated_labels_sil\n  Best score: 0.12559626128378212%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration isolated_labels_sil", 
-        "value": 0.22483985477325863, 
-        "severity": 0, 
-        "severity_value": -0.22483985477325863, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: isolated_labels_sil\n  Worst score: 0.22483985477325863%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration isolated_labels_sil", 
-        "value": 0.4034934324561172, 
-        "severity": 0, 
-        "severity_value": 0.2017467162280586, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: isolated_labels_sil\n  Best score: 0.4034934324561172%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled isolated_labels_sil", 
-        "value": 0.28037990462549894, 
-        "severity": 0, 
-        "severity_value": -0.28037990462549894, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.28037990462549894%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled isolated_labels_sil", 
-        "value": 0.3512514509329773, 
-        "severity": 0, 
-        "severity_value": 0.17562572546648866, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.3512514509329773%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled isolated_labels_sil", 
-        "value": 0.2665876762527907, 
-        "severity": 0, 
-        "severity_value": -0.2665876762527907, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.2665876762527907%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled isolated_labels_sil", 
-        "value": 0.33367244093131687, 
-        "severity": 0, 
-        "severity_value": 0.16683622046565844, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.33367244093131687%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled isolated_labels_sil", 
-        "value": 0.3149346751246851, 
-        "severity": 0, 
-        "severity_value": -0.3149346751246851, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.3149346751246851%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled isolated_labels_sil", 
-        "value": 0.3631413108977998, 
-        "severity": 0, 
-        "severity_value": 0.1815706554488999, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.3631413108977998%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled isolated_labels_sil", 
-        "value": 0.26980219657583615, 
-        "severity": 0, 
-        "severity_value": -0.26980219657583615, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.26980219657583615%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled isolated_labels_sil", 
-        "value": 0.3535237746369645, 
-        "severity": 0, 
-        "severity_value": 0.17676188731848225, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.3535237746369645%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled isolated_labels_sil", 
-        "value": -0.006322677130451585, 
-        "severity": 0, 
-        "severity_value": 0.006322677130451585, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: isolated_labels_sil\n  Worst score: -0.006322677130451585%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled isolated_labels_sil", 
-        "value": 0.2826162682982072, 
-        "severity": 0, 
-        "severity_value": 0.1413081341491036, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.2826162682982072%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled isolated_labels_sil", 
-        "value": -0.0062027325388998135, 
-        "severity": 0, 
-        "severity_value": 0.0062027325388998135, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: -0.0062027325388998135%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled isolated_labels_sil", 
-        "value": 0.2825730381806182, 
-        "severity": 0, 
-        "severity_value": 0.1412865190903091, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.2825730381806182%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled isolated_labels_sil", 
-        "value": 0.026890652515223497, 
-        "severity": 0, 
-        "severity_value": -0.026890652515223497, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.026890652515223497%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled isolated_labels_sil", 
-        "value": 0.298140332450444, 
-        "severity": 0, 
-        "severity_value": 0.149070166225222, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.298140332450444%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled isolated_labels_sil", 
-        "value": 0.026440650507256542, 
-        "severity": 0, 
-        "severity_value": -0.026440650507256542, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.026440650507256542%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled isolated_labels_sil", 
-        "value": 0.2981431603397357, 
-        "severity": 0, 
-        "severity_value": 0.14907158016986785, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.2981431603397357%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled isolated_labels_sil", 
-        "value": 0.29895065971822987, 
-        "severity": 0, 
-        "severity_value": -0.29895065971822987, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.29895065971822987%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled isolated_labels_sil", 
-        "value": 0.3726940866950335, 
-        "severity": 0, 
-        "severity_value": 0.18634704334751676, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.3726940866950335%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled isolated_labels_sil", 
-        "value": 0.25257358394620427, 
-        "severity": 0, 
-        "severity_value": -0.25257358394620427, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.25257358394620427%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled isolated_labels_sil", 
-        "value": 0.3655637349436624, 
-        "severity": 0, 
-        "severity_value": 0.1827818674718312, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.3655637349436624%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled isolated_labels_sil", 
-        "value": 0.3208556504263108, 
-        "severity": 0, 
-        "severity_value": -0.3208556504263108, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.3208556504263108%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled isolated_labels_sil", 
-        "value": 0.4799363512370551, 
-        "severity": 0, 
-        "severity_value": 0.23996817561852754, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.4799363512370551%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled isolated_labels_sil", 
-        "value": 0.2773120085960395, 
-        "severity": 0, 
-        "severity_value": -0.2773120085960395, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.2773120085960395%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled isolated_labels_sil", 
-        "value": 0.41047496542722317, 
-        "severity": 0, 
-        "severity_value": 0.20523748271361159, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.41047496542722317%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration isolated_labels_sil", 
-        "value": 0.22483985011697852, 
-        "severity": 0, 
-        "severity_value": -0.22483985011697852, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: isolated_labels_sil\n  Worst score: 0.22483985011697852%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score no_integration isolated_labels_sil", 
-        "value": 0.4034934324561172, 
-        "severity": 0, 
-        "severity_value": 0.2017467162280586, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: isolated_labels_sil\n  Best score: 0.4034934324561172%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration isolated_labels_sil", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: isolated_labels_sil\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score random_integration isolated_labels_sil", 
-        "value": 0.04725008604179699, 
-        "severity": 0, 
-        "severity_value": 0.023625043020898497, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: isolated_labels_sil\n  Best score: 0.04725008604179699%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full isolated_labels_sil", 
-        "value": 0.09748060894316228, 
-        "severity": 0, 
-        "severity_value": -0.09748060894316228, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: isolated_labels_sil\n  Worst score: 0.09748060894316228%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full isolated_labels_sil", 
-        "value": 0.2440227308701414, 
-        "severity": 0, 
-        "severity_value": 0.1220113654350707, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: isolated_labels_sil\n  Best score: 0.2440227308701414%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg isolated_labels_sil", 
-        "value": 0.1190458095490594, 
-        "severity": 0, 
-        "severity_value": -0.1190458095490594, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: isolated_labels_sil\n  Worst score: 0.1190458095490594%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg isolated_labels_sil", 
-        "value": 0.27139692549570293, 
-        "severity": 0, 
-        "severity_value": 0.13569846274785147, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: isolated_labels_sil\n  Best score: 0.27139692549570293%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled isolated_labels_sil", 
-        "value": 0.3293950891617748, 
-        "severity": 0, 
-        "severity_value": -0.3293950891617748, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.3293950891617748%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled isolated_labels_sil", 
-        "value": 0.4545318553506816, 
-        "severity": 0, 
-        "severity_value": 0.2272659276753408, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.4545318553506816%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled isolated_labels_sil", 
-        "value": 0.34568446633694583, 
-        "severity": 0, 
-        "severity_value": -0.34568446633694583, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.34568446633694583%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled isolated_labels_sil", 
-        "value": 0.3831488810527034, 
-        "severity": 0, 
-        "severity_value": 0.1915744405263517, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.3831488810527034%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled isolated_labels_sil", 
-        "value": 0.32412642437445577, 
-        "severity": 0, 
-        "severity_value": -0.32412642437445577, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.32412642437445577%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled isolated_labels_sil", 
-        "value": 0.5703056331010966, 
-        "severity": 0, 
-        "severity_value": 0.2851528165505483, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: isolated_labels_sil\n  Best score: 0.5703056331010966%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled isolated_labels_sil", 
-        "value": 0.3908898233296004, 
-        "severity": 0, 
-        "severity_value": -0.3908898233296004, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Worst score: 0.3908898233296004%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled isolated_labels_sil", 
-        "value": 0.4615694127716279, 
-        "severity": 0, 
-        "severity_value": 0.23078470638581394, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: isolated_labels_sil\n  Best score: 0.4615694127716279%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration kBET", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: kBET\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration kBET", 
-        "value": 0.017428382512087823, 
-        "severity": 0, 
-        "severity_value": 0.008714191256043911, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: kBET\n  Best score: 0.017428382512087823%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration kBET", 
-        "value": 0.9928837881331518, 
-        "severity": 0, 
-        "severity_value": -0.9928837881331518, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: kBET\n  Worst score: 0.9928837881331518%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration kBET", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: kBET\n  Best score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled kBET", 
-        "value": 0.03571511426837763, 
-        "severity": 0, 
-        "severity_value": -0.03571511426837763, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: kBET\n  Worst score: 0.03571511426837763%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled kBET", 
-        "value": 0.1639769144388565, 
-        "severity": 0, 
-        "severity_value": 0.08198845721942825, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: kBET\n  Best score: 0.1639769144388565%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled kBET", 
-        "value": 0.048743973617638284, 
-        "severity": 0, 
-        "severity_value": -0.048743973617638284, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: kBET\n  Worst score: 0.048743973617638284%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled kBET", 
-        "value": 0.11763965073194121, 
-        "severity": 0, 
-        "severity_value": 0.05881982536597061, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: kBET\n  Best score: 0.11763965073194121%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled kBET", 
-        "value": 0.05227389968984764, 
-        "severity": 0, 
-        "severity_value": -0.05227389968984764, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: kBET\n  Worst score: 0.05227389968984764%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled kBET", 
-        "value": 0.19300940098834493, 
-        "severity": 0, 
-        "severity_value": 0.09650470049417247, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: kBET\n  Best score: 0.19300940098834493%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled kBET", 
-        "value": 0.02524921824651359, 
-        "severity": 0, 
-        "severity_value": -0.02524921824651359, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: kBET\n  Worst score: 0.02524921824651359%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled kBET", 
-        "value": 0.11828743914197846, 
-        "severity": 0, 
-        "severity_value": 0.05914371957098923, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: kBET\n  Best score: 0.11828743914197846%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled kBET", 
-        "value": 0.14988189181460634, 
-        "severity": 0, 
-        "severity_value": -0.14988189181460634, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: kBET\n  Worst score: 0.14988189181460634%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled kBET", 
-        "value": 0.33589429148594685, 
-        "severity": 0, 
-        "severity_value": 0.16794714574297342, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: kBET\n  Best score: 0.33589429148594685%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled kBET", 
-        "value": 0.15452130318937662, 
-        "severity": 0, 
-        "severity_value": -0.15452130318937662, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: kBET\n  Worst score: 0.15452130318937662%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled kBET", 
-        "value": 0.3338519377110833, 
-        "severity": 0, 
-        "severity_value": 0.16692596885554165, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: kBET\n  Best score: 0.3338519377110833%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled kBET", 
-        "value": 0.15701175306150697, 
-        "severity": 0, 
-        "severity_value": -0.15701175306150697, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: kBET\n  Worst score: 0.15701175306150697%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled kBET", 
-        "value": 0.37395401141056717, 
-        "severity": 0, 
-        "severity_value": 0.18697700570528358, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: kBET\n  Best score: 0.37395401141056717%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled kBET", 
-        "value": 0.15203804113533959, 
-        "severity": 0, 
-        "severity_value": -0.15203804113533959, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: kBET\n  Worst score: 0.15203804113533959%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled kBET", 
-        "value": 0.37480792762988474, 
-        "severity": 0, 
-        "severity_value": 0.18740396381494237, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: kBET\n  Best score: 0.37480792762988474%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled kBET", 
-        "value": 0.04134137271966221, 
-        "severity": 0, 
-        "severity_value": -0.04134137271966221, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: kBET\n  Worst score: 0.04134137271966221%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled kBET", 
-        "value": 0.18126478161230625, 
-        "severity": 0, 
-        "severity_value": 0.09063239080615312, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: kBET\n  Best score: 0.18126478161230625%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled kBET", 
-        "value": 0.026199307820182507, 
-        "severity": 0, 
-        "severity_value": -0.026199307820182507, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: kBET\n  Worst score: 0.026199307820182507%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled kBET", 
-        "value": 0.12146486388269617, 
-        "severity": 0, 
-        "severity_value": 0.060732431941348086, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: kBET\n  Best score: 0.12146486388269617%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled kBET", 
-        "value": 0.08469085709603578, 
-        "severity": 0, 
-        "severity_value": -0.08469085709603578, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: kBET\n  Worst score: 0.08469085709603578%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled kBET", 
-        "value": 0.1424760636235808, 
-        "severity": 0, 
-        "severity_value": 0.0712380318117904, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: kBET\n  Best score: 0.1424760636235808%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled kBET", 
-        "value": 0.056224092221626276, 
-        "severity": 0, 
-        "severity_value": -0.056224092221626276, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: kBET\n  Worst score: 0.056224092221626276%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled kBET", 
-        "value": 0.1477916681180801, 
-        "severity": 0, 
-        "severity_value": 0.07389583405904004, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: kBET\n  Best score: 0.1477916681180801%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration kBET", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: kBET\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score no_integration kBET", 
-        "value": 0.094701275978164, 
-        "severity": 0, 
-        "severity_value": 0.047350637989082, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: kBET\n  Best score: 0.094701275978164%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration kBET", 
-        "value": 0.2363876838781062, 
-        "severity": 0, 
-        "severity_value": -0.2363876838781062, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: kBET\n  Worst score: 0.2363876838781062%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score random_integration kBET", 
-        "value": 0.4709561391580874, 
-        "severity": 0, 
-        "severity_value": 0.2354780695790437, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: kBET\n  Best score: 0.4709561391580874%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full kBET", 
-        "value": 0.07308550582216355, 
-        "severity": 0, 
-        "severity_value": -0.07308550582216355, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: kBET\n  Worst score: 0.07308550582216355%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full kBET", 
-        "value": 0.2900958209284679, 
-        "severity": 0, 
-        "severity_value": 0.14504791046423396, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: kBET\n  Best score: 0.2900958209284679%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg kBET", 
-        "value": 0.0539659127960188, 
-        "severity": 0, 
-        "severity_value": -0.0539659127960188, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: kBET\n  Worst score: 0.0539659127960188%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg kBET", 
-        "value": 0.2503310015233925, 
-        "severity": 0, 
-        "severity_value": 0.12516550076169625, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: kBET\n  Best score: 0.2503310015233925%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled kBET", 
-        "value": 0.12711605034617682, 
-        "severity": 0, 
-        "severity_value": -0.12711605034617682, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: kBET\n  Worst score: 0.12711605034617682%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled kBET", 
-        "value": 0.1948704720953535, 
-        "severity": 0, 
-        "severity_value": 0.09743523604767675, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: kBET\n  Best score: 0.1948704720953535%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled kBET", 
-        "value": 0.07141148679499791, 
-        "severity": 0, 
-        "severity_value": -0.07141148679499791, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: kBET\n  Worst score: 0.07141148679499791%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled kBET", 
-        "value": 0.18132818367076475, 
-        "severity": 0, 
-        "severity_value": 0.09066409183538238, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: kBET\n  Best score: 0.18132818367076475%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled kBET", 
-        "value": 0.11177163734378821, 
-        "severity": 0, 
-        "severity_value": -0.11177163734378821, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: kBET\n  Worst score: 0.11177163734378821%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled kBET", 
-        "value": 0.2572400918922619, 
-        "severity": 0, 
-        "severity_value": 0.12862004594613094, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: kBET\n  Best score: 0.2572400918922619%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled kBET", 
-        "value": 0.049604093167111105, 
-        "severity": 0, 
-        "severity_value": -0.049604093167111105, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: kBET\n  Worst score: 0.049604093167111105%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled kBET", 
-        "value": 0.19894669929121878, 
-        "severity": 0, 
-        "severity_value": 0.09947334964560939, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: kBET\n  Best score: 0.19894669929121878%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration nmi", 
-        "value": 0.04467719169833509, 
-        "severity": 0, 
-        "severity_value": -0.04467719169833509, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: nmi\n  Worst score: 0.04467719169833509%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration nmi", 
-        "value": 0.30275088235717657, 
-        "severity": 0, 
-        "severity_value": 0.15137544117858828, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: nmi\n  Best score: 0.30275088235717657%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration nmi", 
-        "value": 0.5950776674946684, 
-        "severity": 0, 
-        "severity_value": -0.5950776674946684, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: nmi\n  Worst score: 0.5950776674946684%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration nmi", 
-        "value": 0.6964045694716459, 
-        "severity": 0, 
-        "severity_value": 0.34820228473582293, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: nmi\n  Best score: 0.6964045694716459%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled nmi", 
-        "value": 0.6863734245869086, 
-        "severity": 0, 
-        "severity_value": -0.6863734245869086, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: nmi\n  Worst score: 0.6863734245869086%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled nmi", 
-        "value": 0.8011861014522996, 
-        "severity": 0, 
-        "severity_value": 0.4005930507261498, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: nmi\n  Best score: 0.8011861014522996%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled nmi", 
-        "value": 0.6932577872189081, 
-        "severity": 0, 
-        "severity_value": -0.6932577872189081, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: nmi\n  Worst score: 0.6932577872189081%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled nmi", 
-        "value": 0.8107749672356598, 
-        "severity": 0, 
-        "severity_value": 0.4053874836178299, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: nmi\n  Best score: 0.8107749672356598%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled nmi", 
-        "value": 0.7085527093825086, 
-        "severity": 0, 
-        "severity_value": -0.7085527093825086, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.7085527093825086%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled nmi", 
-        "value": 0.9184094890971938, 
-        "severity": 0, 
-        "severity_value": 0.4592047445485969, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: nmi\n  Best score: 0.9184094890971938%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled nmi", 
-        "value": 0.7139531833209338, 
-        "severity": 0, 
-        "severity_value": -0.7139531833209338, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7139531833209338%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled nmi", 
-        "value": 0.9086112196345321, 
-        "severity": 0, 
-        "severity_value": 0.45430560981726603, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.9086112196345321%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled nmi", 
-        "value": 0.6648592950135933, 
-        "severity": 0, 
-        "severity_value": -0.6648592950135933, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: nmi\n  Worst score: 0.6648592950135933%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled nmi", 
-        "value": 0.8329812600377606, 
-        "severity": 0, 
-        "severity_value": 0.4164906300188803, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: nmi\n  Best score: 0.8329812600377606%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled nmi", 
-        "value": 0.6651629429231075, 
-        "severity": 0, 
-        "severity_value": -0.6651629429231075, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: nmi\n  Worst score: 0.6651629429231075%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled nmi", 
-        "value": 0.8334625702000381, 
-        "severity": 0, 
-        "severity_value": 0.41673128510001906, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: nmi\n  Best score: 0.8334625702000381%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled nmi", 
-        "value": 0.7200541017450743, 
-        "severity": 0, 
-        "severity_value": -0.7200541017450743, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.7200541017450743%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled nmi", 
-        "value": 0.8751465589851685, 
-        "severity": 0, 
-        "severity_value": 0.43757327949258423, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: nmi\n  Best score: 0.8751465589851685%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled nmi", 
-        "value": 0.7218871310298306, 
-        "severity": 0, 
-        "severity_value": -0.7218871310298306, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7218871310298306%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled nmi", 
-        "value": 0.8412931387417674, 
-        "severity": 0, 
-        "severity_value": 0.4206465693708837, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.8412931387417674%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled nmi", 
-        "value": 0.6981847876650638, 
-        "severity": 0, 
-        "severity_value": -0.6981847876650638, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: nmi\n  Worst score: 0.6981847876650638%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled nmi", 
-        "value": 0.7811989632187101, 
-        "severity": 0, 
-        "severity_value": 0.39059948160935504, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: nmi\n  Best score: 0.7811989632187101%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled nmi", 
-        "value": 0.6412803531625162, 
-        "severity": 0, 
-        "severity_value": -0.6412803531625162, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: nmi\n  Worst score: 0.6412803531625162%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled nmi", 
-        "value": 0.7344507651852203, 
-        "severity": 0, 
-        "severity_value": 0.36722538259261017, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: nmi\n  Best score: 0.7344507651852203%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled nmi", 
-        "value": 0.7290708902304428, 
-        "severity": 0, 
-        "severity_value": -0.7290708902304428, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.7290708902304428%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled nmi", 
-        "value": 0.9151127061014533, 
-        "severity": 0, 
-        "severity_value": 0.45755635305072667, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: nmi\n  Best score: 0.9151127061014533%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled nmi", 
-        "value": 0.7371774568461765, 
-        "severity": 0, 
-        "severity_value": -0.7371774568461765, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7371774568461765%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled nmi", 
-        "value": 0.8391859414686431, 
-        "severity": 0, 
-        "severity_value": 0.41959297073432156, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.8391859414686431%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration nmi", 
-        "value": 0.593701288883777, 
-        "severity": 0, 
-        "severity_value": -0.593701288883777, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: nmi\n  Worst score: 0.593701288883777%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score no_integration nmi", 
-        "value": 0.6950617401102714, 
-        "severity": 0, 
-        "severity_value": 0.3475308700551357, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: nmi\n  Best score: 0.6950617401102714%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration nmi", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: nmi\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score random_integration nmi", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: nmi\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full nmi", 
-        "value": 0.6644264128908601, 
-        "severity": 0, 
-        "severity_value": -0.6644264128908601, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: nmi\n  Worst score: 0.6644264128908601%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full nmi", 
-        "value": 0.8362999831145966, 
-        "severity": 0, 
-        "severity_value": 0.4181499915572983, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: nmi\n  Best score: 0.8362999831145966%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg nmi", 
-        "value": 0.6734989100589676, 
-        "severity": 0, 
-        "severity_value": -0.6734989100589676, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: nmi\n  Worst score: 0.6734989100589676%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg nmi", 
-        "value": 0.8840313784843794, 
-        "severity": 0, 
-        "severity_value": 0.4420156892421897, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: nmi\n  Best score: 0.8840313784843794%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled nmi", 
-        "value": 0.6732578229548133, 
-        "severity": 0, 
-        "severity_value": -0.6732578229548133, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: nmi\n  Worst score: 0.6732578229548133%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled nmi", 
-        "value": 0.7670890696574529, 
-        "severity": 0, 
-        "severity_value": 0.38354453482872647, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: nmi\n  Best score: 0.7670890696574529%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled nmi", 
-        "value": 0.6847375979687779, 
-        "severity": 0, 
-        "severity_value": -0.6847375979687779, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: nmi\n  Worst score: 0.6847375979687779%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled nmi", 
-        "value": 0.7137373801155003, 
-        "severity": 0, 
-        "severity_value": 0.3568686900577501, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: nmi\n  Best score: 0.7137373801155003%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled nmi", 
-        "value": 0.7004249915273513, 
-        "severity": 0, 
-        "severity_value": -0.7004249915273513, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.7004249915273513%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled nmi", 
-        "value": 0.9082061064612876, 
-        "severity": 0, 
-        "severity_value": 0.4541030532306438, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: nmi\n  Best score: 0.9082061064612876%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled nmi", 
-        "value": 0.7170253168507824, 
-        "severity": 0, 
-        "severity_value": -0.7170253168507824, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7170253168507824%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled nmi", 
-        "value": 0.8298610160825192, 
-        "severity": 0, 
-        "severity_value": 0.4149305080412596, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.8298610160825192%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration pcr", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: pcr\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration pcr", 
-        "value": 4.332936215658266e-05, 
-        "severity": 0, 
-        "severity_value": 2.166468107829133e-05, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: pcr\n  Best score: 4.332936215658266e-05%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration pcr", 
-        "value": 0.645998448946277, 
-        "severity": 0, 
-        "severity_value": -0.645998448946277, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: pcr\n  Worst score: 0.645998448946277%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration pcr", 
-        "value": 0.9571737679026353, 
-        "severity": 0, 
-        "severity_value": 0.47858688395131765, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: pcr\n  Best score: 0.9571737679026353%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled pcr", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: pcr\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled pcr", 
-        "value": 1.0000000000364113, 
-        "severity": 0, 
-        "severity_value": 0.5000000000182057, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: pcr\n  Best score: 1.0000000000364113%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled pcr", 
-        "value": 0.9998997919279521, 
-        "severity": 0, 
-        "severity_value": -0.9998997919279521, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: pcr\n  Worst score: 0.9998997919279521%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled pcr", 
-        "value": 0.9999725052028405, 
-        "severity": 0, 
-        "severity_value": 0.49998625260142027, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: pcr\n  Best score: 0.9999725052028405%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled pcr", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: pcr\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled pcr", 
-        "value": 1.0000000000364113, 
-        "severity": 0, 
-        "severity_value": 0.5000000000182057, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: pcr\n  Best score: 1.0000000000364113%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled pcr", 
-        "value": 0.9999549137748108, 
-        "severity": 0, 
-        "severity_value": -0.9999549137748108, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: pcr\n  Worst score: 0.9999549137748108%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled pcr", 
-        "value": 0.9999916990943661, 
-        "severity": 0, 
-        "severity_value": 0.49999584954718307, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: pcr\n  Best score: 0.9999916990943661%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled pcr", 
-        "value": 0.6373509289222175, 
-        "severity": 0, 
-        "severity_value": -0.6373509289222175, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: pcr\n  Worst score: 0.6373509289222175%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled pcr", 
-        "value": 0.8642253952582876, 
-        "severity": 0, 
-        "severity_value": 0.4321126976291438, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: pcr\n  Best score: 0.8642253952582876%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled pcr", 
-        "value": 0.6373354949931658, 
-        "severity": 0, 
-        "severity_value": -0.6373354949931658, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: pcr\n  Worst score: 0.6373354949931658%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled pcr", 
-        "value": 0.8642237146753261, 
-        "severity": 0, 
-        "severity_value": 0.43211185733766305, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: pcr\n  Best score: 0.8642237146753261%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled pcr", 
-        "value": 0.45191166467081495, 
-        "severity": 0, 
-        "severity_value": -0.45191166467081495, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: pcr\n  Worst score: 0.45191166467081495%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled pcr", 
-        "value": 0.8598871679424577, 
-        "severity": 0, 
-        "severity_value": 0.42994358397122884, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: pcr\n  Best score: 0.8598871679424577%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled pcr", 
-        "value": 0.4517304631876, 
-        "severity": 0, 
-        "severity_value": -0.4517304631876, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: pcr\n  Worst score: 0.4517304631876%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled pcr", 
-        "value": 0.8598887547799883, 
-        "severity": 0, 
-        "severity_value": 0.42994437738999414, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: pcr\n  Best score: 0.8598887547799883%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled pcr", 
-        "value": 0.9192050911353934, 
-        "severity": 0, 
-        "severity_value": -0.9192050911353934, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: pcr\n  Worst score: 0.9192050911353934%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled pcr", 
-        "value": 0.9534190302921435, 
-        "severity": 0, 
-        "severity_value": 0.47670951514607174, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: pcr\n  Best score: 0.9534190302921435%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled pcr", 
-        "value": 0.5960701315348713, 
-        "severity": 0, 
-        "severity_value": -0.5960701315348713, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: pcr\n  Worst score: 0.5960701315348713%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled pcr", 
-        "value": 0.8567518075136983, 
-        "severity": 0, 
-        "severity_value": 0.42837590375684914, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: pcr\n  Best score: 0.8567518075136983%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled pcr", 
-        "value": 0.8409023870863225, 
-        "severity": 0, 
-        "severity_value": -0.8409023870863225, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: pcr\n  Worst score: 0.8409023870863225%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled pcr", 
-        "value": 0.924623998917968, 
-        "severity": 0, 
-        "severity_value": 0.462311999458984, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: pcr\n  Best score: 0.924623998917968%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled pcr", 
-        "value": 0.5312664687790407, 
-        "severity": 0, 
-        "severity_value": -0.5312664687790407, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: pcr\n  Worst score: 0.5312664687790407%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled pcr", 
-        "value": 0.8646854276136182, 
-        "severity": 0, 
-        "severity_value": 0.4323427138068091, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: pcr\n  Best score: 0.8646854276136182%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration pcr", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: pcr\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score no_integration pcr", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: pcr\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration pcr", 
-        "value": 0.9989820600561896, 
-        "severity": 0, 
-        "severity_value": -0.9989820600561896, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: pcr\n  Worst score: 0.9989820600561896%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score random_integration pcr", 
-        "value": 0.999770828457825, 
-        "severity": 0, 
-        "severity_value": 0.4998854142289125, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: pcr\n  Best score: 0.999770828457825%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full pcr", 
-        "value": 0.9989775493957257, 
-        "severity": 0, 
-        "severity_value": -0.9989775493957257, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: pcr\n  Worst score: 0.9989775493957257%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full pcr", 
-        "value": 0.9998195875210946, 
-        "severity": 0, 
-        "severity_value": 0.4999097937605473, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: pcr\n  Best score: 0.9998195875210946%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg pcr", 
-        "value": 0.9970158243342768, 
-        "severity": 0, 
-        "severity_value": -0.9970158243342768, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: pcr\n  Worst score: 0.9970158243342768%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg pcr", 
-        "value": 0.9995424193650723, 
-        "severity": 0, 
-        "severity_value": 0.49977120968253613, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: pcr\n  Best score: 0.9995424193650723%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled pcr", 
-        "value": 0.6237096303490666, 
-        "severity": 0, 
-        "severity_value": -0.6237096303490666, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: pcr\n  Worst score: 0.6237096303490666%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled pcr", 
-        "value": 0.9169674317112688, 
-        "severity": 0, 
-        "severity_value": 0.4584837158556344, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: pcr\n  Best score: 0.9169674317112688%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled pcr", 
-        "value": 0.25018575850084196, 
-        "severity": 0, 
-        "severity_value": -0.25018575850084196, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: pcr\n  Worst score: 0.25018575850084196%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled pcr", 
-        "value": 0.6289350246810145, 
-        "severity": 0, 
-        "severity_value": 0.31446751234050724, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: pcr\n  Best score: 0.6289350246810145%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled pcr", 
-        "value": 0.6453699142323122, 
-        "severity": 0, 
-        "severity_value": -0.6453699142323122, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: pcr\n  Worst score: 0.6453699142323122%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled pcr", 
-        "value": 0.8945024953521872, 
-        "severity": 0, 
-        "severity_value": 0.4472512476760936, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: pcr\n  Best score: 0.8945024953521872%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled pcr", 
-        "value": 0.30177326882208033, 
-        "severity": 0, 
-        "severity_value": -0.30177326882208033, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: pcr\n  Worst score: 0.30177326882208033%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled pcr", 
-        "value": 0.801984103103795, 
-        "severity": 0, 
-        "severity_value": 0.4009920515518975, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: pcr\n  Best score: 0.801984103103795%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration silhouette", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: silhouette\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration silhouette", 
-        "value": 0.02307782848442161, 
-        "severity": 0, 
-        "severity_value": 0.011538914242210804, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: silhouette\n  Best score: 0.02307782848442161%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration silhouette", 
-        "value": 0.1339370645745891, 
-        "severity": 0, 
-        "severity_value": -0.1339370645745891, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: silhouette\n  Worst score: 0.1339370645745891%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration silhouette", 
-        "value": 0.19027133920185832, 
-        "severity": 0, 
-        "severity_value": 0.09513566960092916, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: silhouette\n  Best score: 0.19027133920185832%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled silhouette", 
-        "value": 0.21757730361423477, 
-        "severity": 0, 
-        "severity_value": -0.21757730361423477, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: silhouette\n  Worst score: 0.21757730361423477%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled silhouette", 
-        "value": 0.2506557244459512, 
-        "severity": 0, 
-        "severity_value": 0.1253278622229756, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: silhouette\n  Best score: 0.2506557244459512%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled silhouette", 
-        "value": 0.21537824977502817, 
-        "severity": 0, 
-        "severity_value": -0.21537824977502817, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: silhouette\n  Worst score: 0.21537824977502817%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled silhouette", 
-        "value": 0.2505644011717074, 
-        "severity": 0, 
-        "severity_value": 0.1252822005858537, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: silhouette\n  Best score: 0.2505644011717074%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled silhouette", 
-        "value": 0.2519029324492584, 
-        "severity": 0, 
-        "severity_value": -0.2519029324492584, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: silhouette\n  Worst score: 0.2519029324492584%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled silhouette", 
-        "value": 0.31178457904702817, 
-        "severity": 0, 
-        "severity_value": 0.15589228952351408, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: silhouette\n  Best score: 0.31178457904702817%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled silhouette", 
-        "value": 0.2355836064575702, 
-        "severity": 0, 
-        "severity_value": -0.2355836064575702, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: silhouette\n  Worst score: 0.2355836064575702%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled silhouette", 
-        "value": 0.30056702879812314, 
-        "severity": 0, 
-        "severity_value": 0.15028351439906157, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: silhouette\n  Best score: 0.30056702879812314%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled silhouette", 
-        "value": 0.18048554580135642, 
-        "severity": 0, 
-        "severity_value": -0.18048554580135642, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: silhouette\n  Worst score: 0.18048554580135642%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled silhouette", 
-        "value": 0.4945791675222188, 
-        "severity": 0, 
-        "severity_value": 0.2472895837611094, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: silhouette\n  Best score: 0.4945791675222188%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled silhouette", 
-        "value": 0.18048166098594873, 
-        "severity": 0, 
-        "severity_value": -0.18048166098594873, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: silhouette\n  Worst score: 0.18048166098594873%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled silhouette", 
-        "value": 0.4945811381231924, 
-        "severity": 0, 
-        "severity_value": 0.2472905690615962, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: silhouette\n  Best score: 0.4945811381231924%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled silhouette", 
-        "value": 0.2872625486888238, 
-        "severity": 0, 
-        "severity_value": -0.2872625486888238, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: silhouette\n  Worst score: 0.2872625486888238%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled silhouette", 
-        "value": 0.5560187288927168, 
-        "severity": 0, 
-        "severity_value": 0.2780093644463584, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: silhouette\n  Best score: 0.5560187288927168%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled silhouette", 
-        "value": 0.28725647594250603, 
-        "severity": 0, 
-        "severity_value": -0.28725647594250603, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: silhouette\n  Worst score: 0.28725647594250603%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled silhouette", 
-        "value": 0.5560187288927168, 
-        "severity": 0, 
-        "severity_value": 0.2780093644463584, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: silhouette\n  Best score: 0.5560187288927168%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled silhouette", 
-        "value": 0.21210220985608658, 
-        "severity": 0, 
-        "severity_value": -0.21210220985608658, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: silhouette\n  Worst score: 0.21210220985608658%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled silhouette", 
-        "value": 0.25051375627247974, 
-        "severity": 0, 
-        "severity_value": 0.12525687813623987, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: silhouette\n  Best score: 0.25051375627247974%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled silhouette", 
-        "value": 0.228656419144747, 
-        "severity": 0, 
-        "severity_value": -0.228656419144747, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: silhouette\n  Worst score: 0.228656419144747%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled silhouette", 
-        "value": 0.2586578634967051, 
-        "severity": 0, 
-        "severity_value": 0.12932893174835255, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: silhouette\n  Best score: 0.2586578634967051%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled silhouette", 
-        "value": 0.2633164917941881, 
-        "severity": 0, 
-        "severity_value": -0.2633164917941881, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: silhouette\n  Worst score: 0.2633164917941881%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled silhouette", 
-        "value": 0.3118080346725052, 
-        "severity": 0, 
-        "severity_value": 0.1559040173362526, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: silhouette\n  Best score: 0.3118080346725052%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled silhouette", 
-        "value": 0.28441925276158786, 
-        "severity": 0, 
-        "severity_value": -0.28441925276158786, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: silhouette\n  Worst score: 0.28441925276158786%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled silhouette", 
-        "value": 0.3262704394342325, 
-        "severity": 0, 
-        "severity_value": 0.16313521971711625, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: silhouette\n  Best score: 0.3262704394342325%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration silhouette", 
-        "value": 0.1339370645745891, 
-        "severity": 0, 
-        "severity_value": -0.1339370645745891, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: silhouette\n  Worst score: 0.1339370645745891%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score no_integration silhouette", 
-        "value": 0.19027133275520616, 
-        "severity": 0, 
-        "severity_value": 0.09513566637760308, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: silhouette\n  Best score: 0.19027133275520616%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration silhouette", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: silhouette\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score random_integration silhouette", 
-        "value": 0.13308617327445027, 
-        "severity": 0, 
-        "severity_value": 0.06654308663722514, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: silhouette\n  Best score: 0.13308617327445027%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full silhouette", 
-        "value": 0.15175476706303195, 
-        "severity": 0, 
-        "severity_value": -0.15175476706303195, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: silhouette\n  Worst score: 0.15175476706303195%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full silhouette", 
-        "value": 0.21810534256677577, 
-        "severity": 0, 
-        "severity_value": 0.10905267128338789, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: silhouette\n  Best score: 0.21810534256677577%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg silhouette", 
-        "value": 0.17192288766773398, 
-        "severity": 0, 
-        "severity_value": -0.17192288766773398, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: silhouette\n  Worst score: 0.17192288766773398%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg silhouette", 
-        "value": 0.3252905854695862, 
-        "severity": 0, 
-        "severity_value": 0.1626452927347931, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: silhouette\n  Best score: 0.3252905854695862%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled silhouette", 
-        "value": 0.20349235169251068, 
-        "severity": 0, 
-        "severity_value": -0.20349235169251068, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: silhouette\n  Worst score: 0.20349235169251068%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled silhouette", 
-        "value": 0.2681958787403814, 
-        "severity": 0, 
-        "severity_value": 0.1340979393701907, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: silhouette\n  Best score: 0.2681958787403814%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled silhouette", 
-        "value": 0.21447087185133643, 
-        "severity": 0, 
-        "severity_value": -0.21447087185133643, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: silhouette\n  Worst score: 0.21447087185133643%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled silhouette", 
-        "value": 0.24656975895851602, 
-        "severity": 0, 
-        "severity_value": 0.12328487947925801, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: silhouette\n  Best score: 0.24656975895851602%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled silhouette", 
-        "value": 0.2611093948492273, 
-        "severity": 0, 
-        "severity_value": -0.2611093948492273, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: silhouette\n  Worst score: 0.2611093948492273%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled silhouette", 
-        "value": 0.3964417063491306, 
-        "severity": 0, 
-        "severity_value": 0.1982208531745653, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: silhouette\n  Best score: 0.3964417063491306%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled silhouette", 
-        "value": 0.2879578536535502, 
-        "severity": 0, 
-        "severity_value": -0.2879578536535502, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: silhouette\n  Worst score: 0.2879578536535502%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled silhouette", 
-        "value": 0.37181822610072074, 
-        "severity": 0, 
-        "severity_value": 0.18590911305036037, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: silhouette\n  Best score: 0.37181822610072074%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration silhouette_batch", 
-        "value": 0.3801910129411937, 
-        "severity": 0, 
-        "severity_value": -0.3801910129411937, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: silhouette_batch\n  Worst score: 0.3801910129411937%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration silhouette_batch", 
-        "value": 0.4859902413308142, 
-        "severity": 0, 
-        "severity_value": 0.2429951206654071, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: batch_random_integration\n  Metric id: silhouette_batch\n  Best score: 0.4859902413308142%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration silhouette_batch", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: silhouette_batch\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration silhouette_batch", 
-        "value": 0.716775914911191, 
-        "severity": 0, 
-        "severity_value": 0.3583879574555955, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: celltype_random_integration\n  Metric id: silhouette_batch\n  Best score: 0.716775914911191%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled silhouette_batch", 
-        "value": 0.5065773867510295, 
-        "severity": 0, 
-        "severity_value": -0.5065773867510295, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.5065773867510295%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled silhouette_batch", 
-        "value": 0.5198038314885434, 
-        "severity": 0, 
-        "severity_value": 0.2599019157442717, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_scaled\n  Metric id: silhouette_batch\n  Best score: 0.5198038314885434%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled silhouette_batch", 
-        "value": 0.49775424660204237, 
-        "severity": 0, 
-        "severity_value": -0.49775424660204237, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.49775424660204237%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled silhouette_batch", 
-        "value": 0.5823916637827669, 
-        "severity": 0, 
-        "severity_value": 0.2911958318913834, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_full_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.5823916637827669%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled silhouette_batch", 
-        "value": 0.4506924272503471, 
-        "severity": 0, 
-        "severity_value": -0.4506924272503471, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.4506924272503471%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled silhouette_batch", 
-        "value": 0.5110670923796049, 
-        "severity": 0, 
-        "severity_value": 0.25553354618980245, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_scaled\n  Metric id: silhouette_batch\n  Best score: 0.5110670923796049%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled silhouette_batch", 
-        "value": 0.5102699427654983, 
-        "severity": 0, 
-        "severity_value": -0.5102699427654983, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.5102699427654983%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled silhouette_batch", 
-        "value": 0.6153069565481524, 
-        "severity": 0, 
-        "severity_value": 0.3076534782740762, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: combat_hvg_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.6153069565481524%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled silhouette_batch", 
-        "value": 0.2528339618765328, 
-        "severity": 0, 
-        "severity_value": -0.2528339618765328, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.2528339618765328%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled silhouette_batch", 
-        "value": 0.46428192003500907, 
-        "severity": 0, 
-        "severity_value": 0.23214096001750453, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_scaled\n  Metric id: silhouette_batch\n  Best score: 0.46428192003500907%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled silhouette_batch", 
-        "value": 0.2528387563036367, 
-        "severity": 0, 
-        "severity_value": -0.2528387563036367, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.2528387563036367%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled silhouette_batch", 
-        "value": 0.4642953068615548, 
-        "severity": 0, 
-        "severity_value": 0.2321476534307774, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.4642953068615548%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled silhouette_batch", 
-        "value": 0.1752848315721599, 
-        "severity": 0, 
-        "severity_value": -0.1752848315721599, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.1752848315721599%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled silhouette_batch", 
-        "value": 0.4603456925142892, 
-        "severity": 0, 
-        "severity_value": 0.2301728462571446, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: silhouette_batch\n  Best score: 0.4603456925142892%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled silhouette_batch", 
-        "value": 0.17528548738928793, 
-        "severity": 0, 
-        "severity_value": -0.17528548738928793, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.17528548738928793%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled silhouette_batch", 
-        "value": 0.4603549451637314, 
-        "severity": 0, 
-        "severity_value": 0.2301774725818657, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.4603549451637314%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled silhouette_batch", 
-        "value": 0.5855205614351864, 
-        "severity": 0, 
-        "severity_value": -0.5855205614351864, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.5855205614351864%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled silhouette_batch", 
-        "value": 0.6182722405986502, 
-        "severity": 0, 
-        "severity_value": 0.3091361202993251, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_scaled\n  Metric id: silhouette_batch\n  Best score: 0.6182722405986502%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled silhouette_batch", 
-        "value": 0.38955208988466344, 
-        "severity": 0, 
-        "severity_value": -0.38955208988466344, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.38955208988466344%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled silhouette_batch", 
-        "value": 0.5273343197897851, 
-        "severity": 0, 
-        "severity_value": 0.26366715989489253, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_full_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.5273343197897851%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled silhouette_batch", 
-        "value": 0.6305057957171695, 
-        "severity": 0, 
-        "severity_value": -0.6305057957171695, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.6305057957171695%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled silhouette_batch", 
-        "value": 0.6444780238680358, 
-        "severity": 0, 
-        "severity_value": 0.3222390119340179, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_scaled\n  Metric id: silhouette_batch\n  Best score: 0.6444780238680358%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled silhouette_batch", 
-        "value": 0.5881972326556187, 
-        "severity": 0, 
-        "severity_value": -0.5881972326556187, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.5881972326556187%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled silhouette_batch", 
-        "value": 0.6440143045561467, 
-        "severity": 0, 
-        "severity_value": 0.32200715227807336, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: mnn_hvg_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.6440143045561467%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration silhouette_batch", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: silhouette_batch\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score no_integration silhouette_batch", 
-        "value": 0.2852097643938185, 
-        "severity": 0, 
-        "severity_value": 0.14260488219690926, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: no_integration\n  Metric id: silhouette_batch\n  Best score: 0.2852097643938185%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration silhouette_batch", 
-        "value": 0.24403019308017682, 
-        "severity": 0, 
-        "severity_value": -0.24403019308017682, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: silhouette_batch\n  Worst score: 0.24403019308017682%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score random_integration silhouette_batch", 
-        "value": 0.6731464471510875, 
-        "severity": 0, 
-        "severity_value": 0.33657322357554376, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: random_integration\n  Metric id: silhouette_batch\n  Best score: 0.6731464471510875%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full silhouette_batch", 
-        "value": 0.43243371355663424, 
-        "severity": 0, 
-        "severity_value": -0.43243371355663424, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: silhouette_batch\n  Worst score: 0.43243371355663424%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full silhouette_batch", 
-        "value": 0.5319508907580813, 
-        "severity": 0, 
-        "severity_value": 0.26597544537904066, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_full\n  Metric id: silhouette_batch\n  Best score: 0.5319508907580813%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg silhouette_batch", 
-        "value": 0.41397529770603514, 
-        "severity": 0, 
-        "severity_value": -0.41397529770603514, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: silhouette_batch\n  Worst score: 0.41397529770603514%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg silhouette_batch", 
-        "value": 0.5280553080295283, 
-        "severity": 0, 
-        "severity_value": 0.26402765401476413, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scalex_hvg\n  Metric id: silhouette_batch\n  Best score: 0.5280553080295283%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled silhouette_batch", 
-        "value": 0.29526383263126593, 
-        "severity": 0, 
-        "severity_value": -0.29526383263126593, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.29526383263126593%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled silhouette_batch", 
-        "value": 0.44923982343028535, 
-        "severity": 0, 
-        "severity_value": 0.22461991171514267, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_scaled\n  Metric id: silhouette_batch\n  Best score: 0.44923982343028535%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled silhouette_batch", 
-        "value": 0.3318954158034109, 
-        "severity": 0, 
-        "severity_value": -0.3318954158034109, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.3318954158034109%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled silhouette_batch", 
-        "value": 0.5391863135281224, 
-        "severity": 0, 
-        "severity_value": 0.2695931567640612, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_full_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.5391863135281224%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled silhouette_batch", 
-        "value": 0.14924228894423966, 
-        "severity": 0, 
-        "severity_value": -0.14924228894423966, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: silhouette_batch\n  Worst score: 0.14924228894423966%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled silhouette_batch", 
-        "value": 0.4613502045143579, 
-        "severity": 0, 
-        "severity_value": 0.23067510225717894, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: silhouette_batch\n  Best score: 0.4613502045143579%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled silhouette_batch", 
-        "value": 0.32087078092390514, 
-        "severity": 0, 
-        "severity_value": -0.32087078092390514, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: silhouette_batch\n  Worst score: 0.32087078092390514%\n"
-    }, 
-    {
-        "task_id": "batch_integration_feature", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled silhouette_batch", 
-        "value": 0.4266792607730372, 
-        "severity": 0, 
-        "severity_value": 0.2133396303865186, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_feature\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: silhouette_batch\n  Best score: 0.4266792607730372%\n"
-    }
-]
\ No newline at end of file
diff --git a/results/batch_integration_feature/data/results.json b/results/batch_integration_feature/data/results.json
deleted file mode 100644
index e464cc49..00000000
--- a/results/batch_integration_feature/data/results.json
+++ /dev/null
@@ -1,3152 +0,0 @@
-[
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\ No newline at end of file
diff --git a/results/batch_integration_feature/data/task_info.json b/results/batch_integration_feature/data/task_info.json
deleted file mode 100644
index 3cae7061..00000000
--- a/results/batch_integration_feature/data/task_info.json
+++ /dev/null
@@ -1,68 +0,0 @@
-{
-  "task_id": "batch_integration_feature",
-  "commit_sha": "b578c4fb69d5d3d8d3fee7ca1b383f67820dbcca",
-  "task_name": "Batch integration feature",
-  "task_summary": "Removing batch effects while preserving biological variation (feature output)",
-  "task_description": "\nThis is a sub-task of the overall batch integration task. Batch (or data) integration\nintegrates datasets across batches that arise from various biological and technical\nsources. Methods that integrate batches typically have three different types of output:\na corrected feature matrix, a joint embedding across batches, and/or an integrated\ncell-cell similarity graph (e.g., a kNN graph). This sub-task focuses on all methods\nthat can output feature matrices. Other sub-tasks for batch integration can be found\nfor:\n\n* [graphs](../batch_integration_graph/), and\n* [embeddings](../batch_integration_embed/)\n\nThis sub-task was taken from a [benchmarking study of data integration\nmethods](https://openproblems.bio/bibliography#luecken2022benchmarking).\n\n",
-  "repo": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_feature",
-  "authors": [
-    {
-      "name": "Michaela Mueller",
-      "roles": ["maintainer", "author"],
-      "info": {
-        "github": "mumichae",
-        "orcid": "0000-0002-1401-1785"
-      }
-    },
-    {
-      "name": "Malte Luecken",
-      "roles": "author",
-      "info": {
-        "github": "LuckyMD",
-        "orcid": "0000-0001-7464-7921"
-      }
-    },
-    {
-      "name": "Daniel Strobl",
-      "roles": "author",
-      "info": {
-        "github": "danielStrobl",
-        "orcid": "0000-0002-5516-7057"
-      }
-    },
-    {
-      "name": "Robrecht Cannoodt",
-      "roles": "contributor",
-      "info": {
-        "github": "rcannood",
-        "orcid": "0000-0003-3641-729X"
-      }
-    },
-    {
-      "name": "Scott Gigante",
-      "roles": "contributor",
-      "info": {
-        "github": "scottgigante",
-        "orcid": "0000-0002-4544-2764"
-      }
-    },
-    {
-      "name": "Kai Waldrant",
-      "roles": "contributor",
-      "info": {
-        "github": "KaiWaldrant",
-        "orcid": "0009-0003-8555-1361"
-      }
-    },
-    {
-      "name": "Nartin Kim",
-      "roles": "contributor",
-      "info": {
-        "github": "martinkim0",
-        "orcid": "0009-0003-8555-1361"
-      }
-    }
-  ],
-  "version": "v1.0.0",
-  "license": "MIT"
-}
diff --git a/results/batch_integration_feature/index.markdown_strict_files/figure-markdown_strict/raw_results-1.png b/results/batch_integration_feature/index.markdown_strict_files/figure-markdown_strict/raw_results-1.png
deleted file mode 100644
index 3ecb5c8d..00000000
Binary files a/results/batch_integration_feature/index.markdown_strict_files/figure-markdown_strict/raw_results-1.png and /dev/null differ
diff --git a/results/batch_integration_feature/index.markdown_strict_files/figure-markdown_strict/summary-1.png b/results/batch_integration_feature/index.markdown_strict_files/figure-markdown_strict/summary-1.png
deleted file mode 100644
index 7522783a..00000000
Binary files a/results/batch_integration_feature/index.markdown_strict_files/figure-markdown_strict/summary-1.png and /dev/null differ
diff --git a/results/batch_integration_feature/index.qmd b/results/batch_integration_feature/index.qmd
deleted file mode 100644
index c35c7000..00000000
--- a/results/batch_integration_feature/index.qmd
+++ /dev/null
@@ -1,22 +0,0 @@
----
-title: "Batch integration feature"
-subtitle: "Removing batch effects while preserving biological variation (feature output)"
-image: thumbnail.svg
-page-layout: full
-css: ../_include/task_template.css
-engine: knitr
-fig-cap-location: bottom
-citation-location: document
-bibliography: 
-  - library.bib
-  - ../../library.bib
-toc: false
----
-
-```{r}
-#| include: false
-params <- list(data_dir = "results/batch_integration_feature/data")
-params <- list(data_dir = "./data")
-```
-
-{{< include ../_include/_task_template.qmd >}}
diff --git a/results/batch_integration_feature/thumbnail.svg b/results/batch_integration_feature/thumbnail.svg
deleted file mode 100644
index 77626c5b..00000000
--- a/results/batch_integration_feature/thumbnail.svg
+++ /dev/null
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diff --git a/results/batch_integration_graph/data/dataset_info.json b/results/batch_integration_graph/data/dataset_info.json
deleted file mode 100644
index a50182a3..00000000
--- a/results/batch_integration_graph/data/dataset_info.json
+++ /dev/null
@@ -1,38 +0,0 @@
-[
-    {
-        "dataset_name": "Immune (by batch)",
-        "image": "openproblems",
-        "data_url": "https://ndownloader.figshare.com/files/36086786",
-        "data_reference": "luecken2022benchmarking",
-        "dataset_summary": "Human immune cells from peripheral blood and bone marrow taken from 5 datasets comprising 10 batches across technologies (10X, Smart-seq2).",
-        "task_id": "batch_integration_graph",
-        "commit_sha": "9d1665076cf6215a31f89ed2be8be20a02502887",
-        "dataset_id": "immune_batch",
-        "source_dataset_id": "openproblems_v1/immune_cells",
-        "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/_batch_integration/_common/datasets/immune.py"
-    },
-    {
-        "dataset_name": "Lung (Viera Braga et al.)",
-        "image": "openproblems",
-        "data_url": "https://figshare.com/ndownloader/files/24539942",
-        "data_reference": "luecken2022benchmarking",
-        "dataset_summary": "Human lung scRNA-seq data from 3 datasets with 32,472 cells. From Vieira Braga et al. Technologies: 10X and Drop-seq.",
-        "task_id": "batch_integration_graph",
-        "commit_sha": "9d1665076cf6215a31f89ed2be8be20a02502887",
-        "dataset_id": "lung_batch",
-        "source_dataset_id": "openproblems_v1/tabula_muris_senis_droplet_lung",
-        "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/_batch_integration/_common/datasets/lung.py"
-    },
-    {
-        "dataset_name": "Pancreas (by batch)",
-        "image": "openproblems",
-        "data_url": "https://ndownloader.figshare.com/files/36086813",
-        "data_reference": "luecken2022benchmarking",
-        "dataset_summary": "Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq).",
-        "task_id": "batch_integration_graph",
-        "commit_sha": "9d1665076cf6215a31f89ed2be8be20a02502887",
-        "dataset_id": "pancreas_batch",
-        "source_dataset_id": "openproblems_v1/pancreas",
-        "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/_batch_integration/_common/datasets/pancreas.py"
-    }
-]
\ No newline at end of file
diff --git a/results/batch_integration_graph/data/method_info.json b/results/batch_integration_graph/data/method_info.json
deleted file mode 100644
index eab850fd..00000000
--- a/results/batch_integration_graph/data/method_info.json
+++ /dev/null
@@ -1,677 +0,0 @@
-[
-  {
-    "method_name": "Random Integration by Batch",
-    "method_summary": "Feature values, embedding coordinates, and graph connectivity are all randomly permuted within each batch label",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
-    "is_baseline": true,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "batch_random_integration",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/_common/methods/baseline.py"
-  },
-  {
-    "method_name": "BBKNN (full/scaled)",
-    "method_summary": "BBKNN or batch balanced k nearest neighbours graph is built for each cell by identifying its k nearest neighbours within each defined batch separately, creating independent neighbour sets for each cell in each batch. These sets are then combined and processed with the UMAP algorithm for visualisation.",
-    "paper_name": "BBKNN: fast batch alignment of single cell transcriptomes",
-    "paper_reference": "polanski2020bbknn",
-    "paper_year": 2020,
-    "code_url": "https://github.com/Teichlab/bbknn/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "bbknn_full_scaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/bbknn.py"
-  },
-  {
-    "method_name": "BBKNN (full/unscaled)",
-    "method_summary": "BBKNN or batch balanced k nearest neighbours graph is built for each cell by identifying its k nearest neighbours within each defined batch separately, creating independent neighbour sets for each cell in each batch. These sets are then combined and processed with the UMAP algorithm for visualisation.",
-    "paper_name": "BBKNN: fast batch alignment of single cell transcriptomes",
-    "paper_reference": "polanski2020bbknn",
-    "paper_year": 2020,
-    "code_url": "https://github.com/Teichlab/bbknn/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "bbknn_full_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/bbknn.py"
-  },
-  {
-    "method_name": "BBKNN (hvg/scaled)",
-    "method_summary": "BBKNN or batch balanced k nearest neighbours graph is built for each cell by identifying its k nearest neighbours within each defined batch separately, creating independent neighbour sets for each cell in each batch. These sets are then combined and processed with the UMAP algorithm for visualisation.",
-    "paper_name": "BBKNN: fast batch alignment of single cell transcriptomes",
-    "paper_reference": "polanski2020bbknn",
-    "paper_year": 2020,
-    "code_url": "https://github.com/Teichlab/bbknn/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "bbknn_hvg_scaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/bbknn.py"
-  },
-  {
-    "method_name": "BBKNN (hvg/unscaled)",
-    "method_summary": "BBKNN or batch balanced k nearest neighbours graph is built for each cell by identifying its k nearest neighbours within each defined batch separately, creating independent neighbour sets for each cell in each batch. These sets are then combined and processed with the UMAP algorithm for visualisation.",
-    "paper_name": "BBKNN: fast batch alignment of single cell transcriptomes",
-    "paper_reference": "polanski2020bbknn",
-    "paper_year": 2020,
-    "code_url": "https://github.com/Teichlab/bbknn/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "bbknn_hvg_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/bbknn.py"
-  },
-  {
-    "method_name": "Random Graph by Celltype",
-    "method_summary": "Cells are embedded as a one-hot encoding of celltype labels. A graph is then built on this embedding",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
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-  },
-  {
-    "method_name": "Random Integration by Celltype",
-    "method_summary": "Feature values, embedding coordinates, and graph connectivity are all randomly permuted within each celltype label",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
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-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
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-  {
-    "method_name": "Combat (full/scaled)",
-    "method_summary": "ComBat uses an Empirical Bayes (EB) approach to correct for batch effects. It estimates batch-specific parameters by pooling information across genes in each batch and shrinks the estimates towards the overall mean of the batch effect estimates across all genes. These parameters are then used to adjust the data for batch effects, leading to more accurate and reproducible results.",
-    "paper_name": "Adjusting batch effects in microarray expression data using empirical Bayes methods",
-    "paper_reference": "hansen2012removing",
-    "paper_year": 2007,
-    "code_url": "https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.combat.html/tree/v1.0.0/openproblems/tasks",
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-  {
-    "method_name": "Combat (full/unscaled)",
-    "method_summary": "ComBat uses an Empirical Bayes (EB) approach to correct for batch effects. It estimates batch-specific parameters by pooling information across genes in each batch and shrinks the estimates towards the overall mean of the batch effect estimates across all genes. These parameters are then used to adjust the data for batch effects, leading to more accurate and reproducible results.",
-    "paper_name": "Adjusting batch effects in microarray expression data using empirical Bayes methods",
-    "paper_reference": "hansen2012removing",
-    "paper_year": 2007,
-    "code_url": "https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.combat.html/tree/v1.0.0/openproblems/tasks",
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-  },
-  {
-    "method_name": "Combat (hvg/scaled)",
-    "method_summary": "ComBat uses an Empirical Bayes (EB) approach to correct for batch effects. It estimates batch-specific parameters by pooling information across genes in each batch and shrinks the estimates towards the overall mean of the batch effect estimates across all genes. These parameters are then used to adjust the data for batch effects, leading to more accurate and reproducible results.",
-    "paper_name": "Adjusting batch effects in microarray expression data using empirical Bayes methods",
-    "paper_reference": "hansen2012removing",
-    "paper_year": 2007,
-    "code_url": "https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.combat.html/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_graph",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/combat.py"
-  },
-  {
-    "method_name": "Combat (hvg/unscaled)",
-    "method_summary": "ComBat uses an Empirical Bayes (EB) approach to correct for batch effects. It estimates batch-specific parameters by pooling information across genes in each batch and shrinks the estimates towards the overall mean of the batch effect estimates across all genes. These parameters are then used to adjust the data for batch effects, leading to more accurate and reproducible results.",
-    "paper_name": "Adjusting batch effects in microarray expression data using empirical Bayes methods",
-    "paper_reference": "hansen2012removing",
-    "paper_year": 2007,
-    "code_url": "https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.combat.html/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
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-  },
-  {
-    "method_name": "FastMNN embed (full/scaled)",
-    "method_summary": "fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step.",
-    "paper_name": "A description of the theory behind the fastMNN algorithm",
-    "paper_reference": "lun2019fastmnn",
-    "paper_year": 2019,
-    "code_url": "https://doi.org/doi:10.18129/B9.bioc.batchelor/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
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-    "task_id": "batch_integration_graph",
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-    "method_id": "fastmnn_embed_full_scaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py"
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-  {
-    "method_name": "FastMNN embed (full/unscaled)",
-    "method_summary": "fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step.",
-    "paper_name": "A description of the theory behind the fastMNN algorithm",
-    "paper_reference": "lun2019fastmnn",
-    "paper_year": 2019,
-    "code_url": "https://doi.org/doi:10.18129/B9.bioc.batchelor/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py"
-  },
-  {
-    "method_name": "FastMNN embed (hvg/scaled)",
-    "method_summary": "fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step.",
-    "paper_name": "A description of the theory behind the fastMNN algorithm",
-    "paper_reference": "lun2019fastmnn",
-    "paper_year": 2019,
-    "code_url": "https://doi.org/doi:10.18129/B9.bioc.batchelor/tree/v1.0.0/openproblems/tasks",
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-  {
-    "method_name": "FastMNN embed (hvg/unscaled)",
-    "method_summary": "fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step.",
-    "paper_name": "A description of the theory behind the fastMNN algorithm",
-    "paper_reference": "lun2019fastmnn",
-    "paper_year": 2019,
-    "code_url": "https://doi.org/doi:10.18129/B9.bioc.batchelor/tree/v1.0.0/openproblems/tasks",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py"
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-  {
-    "method_name": "FastMNN feature (full/scaled)",
-    "method_summary": "fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step.",
-    "paper_name": "A description of the theory behind the fastMNN algorithm",
-    "paper_reference": "lun2019fastmnn",
-    "paper_year": 2019,
-    "code_url": "https://doi.org/doi:10.18129/B9.bioc.batchelor/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "fastmnn_feature_full_scaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py"
-  },
-  {
-    "method_name": "FastMNN feature (full/unscaled)",
-    "method_summary": "fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step.",
-    "paper_name": "A description of the theory behind the fastMNN algorithm",
-    "paper_reference": "lun2019fastmnn",
-    "paper_year": 2019,
-    "code_url": "https://doi.org/doi:10.18129/B9.bioc.batchelor/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
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-    "task_id": "batch_integration_graph",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py"
-  },
-  {
-    "method_name": "FastMNN feature (hvg/scaled)",
-    "method_summary": "fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step.",
-    "paper_name": "A description of the theory behind the fastMNN algorithm",
-    "paper_reference": "lun2019fastmnn",
-    "paper_year": 2019,
-    "code_url": "https://doi.org/doi:10.18129/B9.bioc.batchelor/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
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-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py"
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-  {
-    "method_name": "FastMNN feature (hvg/unscaled)",
-    "method_summary": "fastMNN performs a multi-sample PCA to reduce dimensionality, identifying MNN paris in the low-dimensional space, and then correcting the target batch towards the reference using locally weighted correction vectors. The corrected target batch is then merged with the reference. The process is repeated with the next target batch except for the PCA step.",
-    "paper_name": "A description of the theory behind the fastMNN algorithm",
-    "paper_reference": "lun2019fastmnn",
-    "paper_year": 2019,
-    "code_url": "https://doi.org/doi:10.18129/B9.bioc.batchelor/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py"
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-  {
-    "method_name": "Harmony (full/scaled)",
-    "method_summary": "Harmony is a method that uses PCA to group the cells into multi-dataset clusters, and then computes cluster-specific linear correction factors. Each cell is then corrected by its cell-specific linear factor using the cluster-weighted average. The method keeps iterating these four steps until cell clusters are stable.",
-    "paper_name": "Fast, sensitive and accurate integration of single-cell data with Harmony",
-    "paper_reference": "korsunsky2019fast",
-    "paper_year": 2019,
-    "code_url": "https://github.com/lilab-bcb/harmony-pytorch/tree/v1.0.0/openproblems/tasks",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/harmony.py"
-  },
-  {
-    "method_name": "Harmony (full/unscaled)",
-    "method_summary": "Harmony is a method that uses PCA to group the cells into multi-dataset clusters, and then computes cluster-specific linear correction factors. Each cell is then corrected by its cell-specific linear factor using the cluster-weighted average. The method keeps iterating these four steps until cell clusters are stable.",
-    "paper_name": "Fast, sensitive and accurate integration of single-cell data with Harmony",
-    "paper_reference": "korsunsky2019fast",
-    "paper_year": 2019,
-    "code_url": "https://github.com/lilab-bcb/harmony-pytorch/tree/v1.0.0/openproblems/tasks",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/harmony.py"
-  },
-  {
-    "method_name": "Harmony (hvg/scaled)",
-    "method_summary": "Harmony is a method that uses PCA to group the cells into multi-dataset clusters, and then computes cluster-specific linear correction factors. Each cell is then corrected by its cell-specific linear factor using the cluster-weighted average. The method keeps iterating these four steps until cell clusters are stable.",
-    "paper_name": "Fast, sensitive and accurate integration of single-cell data with Harmony",
-    "paper_reference": "korsunsky2019fast",
-    "paper_year": 2019,
-    "code_url": "https://github.com/lilab-bcb/harmony-pytorch/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/harmony.py"
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-  {
-    "method_name": "Harmony (hvg/unscaled)",
-    "method_summary": "Harmony is a method that uses PCA to group the cells into multi-dataset clusters, and then computes cluster-specific linear correction factors. Each cell is then corrected by its cell-specific linear factor using the cluster-weighted average. The method keeps iterating these four steps until cell clusters are stable.",
-    "paper_name": "Fast, sensitive and accurate integration of single-cell data with Harmony",
-    "paper_reference": "korsunsky2019fast",
-    "paper_year": 2019,
-    "code_url": "https://github.com/lilab-bcb/harmony-pytorch/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/harmony.py"
-  },
-  {
-    "method_name": "Liger (full/unscaled)",
-    "method_summary": "LIGER or linked inference of genomic experimental relationships uses iNMF deriving and implementing a novel coordinate descent algorithm to efficiently do the factorization. Joint clustering is performed and factor loadings are normalised.",
-    "paper_name": "Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity",
-    "paper_reference": "welch2019single",
-    "paper_year": 2019,
-    "code_url": "https://github.com/welch-lab/liger/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
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-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "liger_full_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/liger.py"
-  },
-  {
-    "method_name": "Liger (hvg/unscaled)",
-    "method_summary": "LIGER or linked inference of genomic experimental relationships uses iNMF deriving and implementing a novel coordinate descent algorithm to efficiently do the factorization. Joint clustering is performed and factor loadings are normalised.",
-    "paper_name": "Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity",
-    "paper_reference": "welch2019single",
-    "paper_year": 2019,
-    "code_url": "https://github.com/welch-lab/liger/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
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-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "liger_hvg_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/liger.py"
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-  {
-    "method_name": "MNN (full/scaled)",
-    "method_summary": "MNN first detects mutual nearest neighbours in two of the batches and infers a projection of the second onto the first batch. After that, additional batches are added iteratively.",
-    "paper_name": "Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors",
-    "paper_reference": "haghverdi2018batch",
-    "paper_year": 2018,
-    "code_url": "https://github.com/chriscainx/mnnpy/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "code_version": "v1.0.0",
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-  {
-    "method_name": "MNN (full/unscaled)",
-    "method_summary": "MNN first detects mutual nearest neighbours in two of the batches and infers a projection of the second onto the first batch. After that, additional batches are added iteratively.",
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-    "paper_reference": "haghverdi2018batch",
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-    "code_url": "https://github.com/chriscainx/mnnpy/tree/v1.0.0/openproblems/tasks",
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-  },
-  {
-    "method_name": "MNN (hvg/scaled)",
-    "method_summary": "MNN first detects mutual nearest neighbours in two of the batches and infers a projection of the second onto the first batch. After that, additional batches are added iteratively.",
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-    "paper_reference": "haghverdi2018batch",
-    "paper_year": 2018,
-    "code_url": "https://github.com/chriscainx/mnnpy/tree/v1.0.0/openproblems/tasks",
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-  },
-  {
-    "method_name": "MNN (hvg/unscaled)",
-    "method_summary": "MNN first detects mutual nearest neighbours in two of the batches and infers a projection of the second onto the first batch. After that, additional batches are added iteratively.",
-    "paper_name": "Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors",
-    "paper_reference": "haghverdi2018batch",
-    "paper_year": 2018,
-    "code_url": "https://github.com/chriscainx/mnnpy/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_graph",
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-  },
-  {
-    "method_name": "No Integration",
-    "method_summary": "Cells are embedded by PCA on the unintegrated data. A graph is built on this PCA embedding.",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
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-  },
-  {
-    "method_name": "Random Integration",
-    "method_summary": "Feature values, embedding coordinates, and graph connectivity are all randomly permuted",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
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-  },
-  {
-    "method_name": "SCALEX (full)",
-    "method_summary": "SCALEX is a method for integrating heterogeneous single-cell data online using a VAE framework. Its generalised encoder disentangles batch-related components from batch-invariant biological components, which are then projected into a common cell-embedding space.",
-    "paper_name": "Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space",
-    "paper_reference": "xiong2021online",
-    "paper_year": 2022,
-    "code_url": "https://github.com/jsxlei/SCALEX/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-pytorch",
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-    "code_version": "v1.0.0",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scalex.py"
-  },
-  {
-    "method_name": "SCALEX (hvg)",
-    "method_summary": "SCALEX is a method for integrating heterogeneous single-cell data online using a VAE framework. Its generalised encoder disentangles batch-related components from batch-invariant biological components, which are then projected into a common cell-embedding space.",
-    "paper_name": "Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space",
-    "paper_reference": "xiong2021online",
-    "paper_year": 2022,
-    "code_url": "https://github.com/jsxlei/SCALEX/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-pytorch",
-    "is_baseline": false,
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-    "method_id": "scalex_hvg",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scalex.py"
-  },
-  {
-    "method_name": "Scanorama (full/scaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-  },
-  {
-    "method_name": "Scanorama (full/unscaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
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-    "method_id": "scanorama_embed_full_unscaled",
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-  },
-  {
-    "method_name": "Scanorama (hvg/scaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
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-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "scanorama_embed_hvg_scaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py"
-  },
-  {
-    "method_name": "Scanorama (hvg/unscaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py"
-  },
-  {
-    "method_name": "Scanorama gene output (full/scaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "scanorama_feature_full_scaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py"
-  },
-  {
-    "method_name": "Scanorama gene output (full/unscaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "scanorama_feature_full_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py"
-  },
-  {
-    "method_name": "Scanorama gene output (hvg/scaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "scanorama_feature_hvg_scaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py"
-  },
-  {
-    "method_name": "Scanorama gene output (hvg/unscaled)",
-    "method_summary": "Scanorama is an extension of the MNN method. Other then MNN, it finds mutual nearest neighbours over all batches and embeds observations into a joint hyperplane.",
-    "paper_name": "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama",
-    "paper_reference": "hie2019efficient",
-    "paper_year": 2019,
-    "code_url": "https://github.com/brianhie/scanorama/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "scanorama_feature_hvg_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py"
-  },
-  {
-    "method_name": "scANVI (full/unscaled)",
-    "method_summary": "ScanVI is an extension of scVI but instead using a Bayesian semi-supervised approach for more principled cell annotation.",
-    "paper_name": "Probabilistic harmonization and annotation of single\u2010cell transcriptomics data with deep generative models",
-    "paper_reference": "xu2021probabilistic",
-    "paper_year": 2021,
-    "code_url": "https://github.com/YosefLab/scvi-tools/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "scanvi_full_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanvi.py"
-  },
-  {
-    "method_name": "scANVI (hvg/unscaled)",
-    "method_summary": "ScanVI is an extension of scVI but instead using a Bayesian semi-supervised approach for more principled cell annotation.",
-    "paper_name": "Probabilistic harmonization and annotation of single\u2010cell transcriptomics data with deep generative models",
-    "paper_reference": "xu2021probabilistic",
-    "paper_year": 2021,
-    "code_url": "https://github.com/YosefLab/scvi-tools/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "scanvi_hvg_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanvi.py"
-  },
-  {
-    "method_name": "scVI (full/unscaled)",
-    "method_summary": "scVI combines a variational autoencoder with a hierarchical Bayesian model.",
-    "paper_name": "Deep generative modeling for single-cell transcriptomics",
-    "paper_reference": "lopez2018deep",
-    "paper_year": 2018,
-    "code_url": "https://github.com/YosefLab/scvi-tools/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
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-    "method_id": "scvi_full_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scvi.py"
-  },
-  {
-    "method_name": "scVI (hvg/unscaled)",
-    "method_summary": "scVI combines a variational autoencoder with a hierarchical Bayesian model.",
-    "paper_name": "Deep generative modeling for single-cell transcriptomics",
-    "paper_reference": "lopez2018deep",
-    "paper_year": 2018,
-    "code_url": "https://github.com/YosefLab/scvi-tools/tree/v1.0.0/openproblems/tasks",
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-    "method_id": "scvi_hvg_unscaled",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scvi.py"
-  }
-]
\ No newline at end of file
diff --git a/results/batch_integration_graph/data/metric_info.json b/results/batch_integration_graph/data/metric_info.json
deleted file mode 100644
index 49a77048..00000000
--- a/results/batch_integration_graph/data/metric_info.json
+++ /dev/null
@@ -1,50 +0,0 @@
-[
-  {
-    "metric_name": "ARI",
-    "metric_summary": "ARI (Adjusted Rand Index) compares the overlap of two clusterings. It considers both correct clustering overlaps while also counting correct disagreements between two clustering.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "metric_id": "ari",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/metrics/ari.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "Graph connectivity",
-    "metric_summary": "The graph connectivity metric assesses whether the kNN graph representation, G, of the integrated data connects all cells with the same cell identity label.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "metric_id": "graph_connectivity",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/metrics/graph_connectivity.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "Isolated label F1",
-    "metric_summary": "Isolated cell labels are identified as the labels present in the least number of batches in the integration task. The score evaluates how well these isolated labels separate from other cell identities based on clustering.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "metric_id": "isolated_labels_f1",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/metrics/iso_label_f1.py",
-    "code_version": "v1.0.0"
-  },
-  {
-    "metric_name": "NMI",
-    "metric_summary": "NMI compares the overlap of two clusterings. We used NMI to compare the cell-type labels with Louvain clusters computed on the integrated dataset.",
-    "paper_reference": "luecken2022benchmarking",
-    "maximize": true,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-pytorch",
-    "task_id": "batch_integration_graph",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "metric_id": "nmi",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph/metrics/nmi.py",
-    "code_version": "v1.0.0"
-  }
-]
\ No newline at end of file
diff --git a/results/batch_integration_graph/data/quality_control.json b/results/batch_integration_graph/data/quality_control.json
deleted file mode 100644
index 754829bd..00000000
--- a/results/batch_integration_graph/data/quality_control.json
+++ /dev/null
@@ -1,4382 +0,0 @@
-[
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Task info", 
-        "name": "Pct 'task_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_id' should be defined\n  Task id: batch_integration_graph\n  Field: task_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Task info", 
-        "name": "Pct 'commit_sha' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'commit_sha' should be defined\n  Task id: batch_integration_graph\n  Field: commit_sha\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Task info", 
-        "name": "Pct 'task_name' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_name' should be defined\n  Task id: batch_integration_graph\n  Field: task_name\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Task info", 
-        "name": "Pct 'task_summary' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_summary' should be defined\n  Task id: batch_integration_graph\n  Field: task_summary\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Task info", 
-        "name": "Pct 'task_description' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_description' should be defined\n  Task id: batch_integration_graph\n  Field: task_description\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Method info", 
-        "name": "Pct 'task_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'task_id' should be defined\n  Task id: batch_integration_graph\n  Field: task_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Method info", 
-        "name": "Pct 'commit_sha' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'commit_sha' should be defined\n  Task id: batch_integration_graph\n  Field: commit_sha\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Method info", 
-        "name": "Pct 'method_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'method_id' should be defined\n  Task id: batch_integration_graph\n  Field: method_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Method info", 
-        "name": "Pct 'method_name' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'method_name' should be defined\n  Task id: batch_integration_graph\n  Field: method_name\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Method info", 
-        "name": "Pct 'method_summary' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'method_summary' should be defined\n  Task id: batch_integration_graph\n  Field: method_summary\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Method info", 
-        "name": "Pct 'paper_reference' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'paper_reference' should be defined\n  Task id: batch_integration_graph\n  Field: paper_reference\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Method info", 
-        "name": "Pct 'is_baseline' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'is_baseline' should be defined\n  Task id: batch_integration_graph\n  Field: is_baseline\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Metric info", 
-        "name": "Pct 'task_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'task_id' should be defined\n  Task id: batch_integration_graph\n  Field: task_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Metric info", 
-        "name": "Pct 'commit_sha' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'commit_sha' should be defined\n  Task id: batch_integration_graph\n  Field: commit_sha\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Metric info", 
-        "name": "Pct 'metric_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'metric_id' should be defined\n  Task id: batch_integration_graph\n  Field: metric_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Metric info", 
-        "name": "Pct 'metric_name' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'metric_name' should be defined\n  Task id: batch_integration_graph\n  Field: metric_name\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Metric info", 
-        "name": "Pct 'metric_summary' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'metric_summary' should be defined\n  Task id: batch_integration_graph\n  Field: metric_summary\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Metric info", 
-        "name": "Pct 'paper_reference' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'paper_reference' should be defined\n  Task id: batch_integration_graph\n  Field: paper_reference\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Metric info", 
-        "name": "Pct 'maximize' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'maximize' should be defined\n  Task id: batch_integration_graph\n  Field: maximize\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Dataset info", 
-        "name": "Pct 'task_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'task_id' should be defined\n  Task id: batch_integration_graph\n  Field: task_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Dataset info", 
-        "name": "Pct 'commit_sha' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'commit_sha' should be defined\n  Task id: batch_integration_graph\n  Field: commit_sha\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Dataset info", 
-        "name": "Pct 'dataset_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'dataset_id' should be defined\n  Task id: batch_integration_graph\n  Field: dataset_id\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Dataset info", 
-        "name": "Pct 'dataset_name' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'dataset_name' should be defined\n  Task id: batch_integration_graph\n  Field: dataset_name\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Dataset info", 
-        "name": "Pct 'dataset_summary' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'dataset_summary' should be defined\n  Task id: batch_integration_graph\n  Field: dataset_summary\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Dataset info", 
-        "name": "Pct 'data_reference' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'data_reference' should be defined\n  Task id: batch_integration_graph\n  Field: data_reference\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw data", 
-        "name": "Number of results", 
-        "value": 135, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "len(results) == len(method_info) * len(metric_info) * len(dataset_info)", 
-        "message": "Number of results should be equal to #methods × #metrics × #datasets.\n  Task id: batch_integration_graph\n  Number of results: 135\n  Number of methods: 45\n  Number of metrics: 4\n  Number of datasets: 3\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Metric 'ari' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  Metric id: ari\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Metric 'graph_connectivity' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  Metric id: graph_connectivity\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Metric 'isolated_labels_f1' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  Metric id: isolated_labels_f1\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Metric 'nmi' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  Metric id: nmi\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'batch_random_integration' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: batch_random_integration\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'bbknn_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: bbknn_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'bbknn_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: bbknn_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'bbknn_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: bbknn_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'bbknn_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: bbknn_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'celltype_random_graph' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: celltype_random_graph\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'celltype_random_integration' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: celltype_random_integration\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'combat_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: combat_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'combat_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: combat_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'combat_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: combat_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'combat_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: combat_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_embed_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: fastmnn_embed_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_embed_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: fastmnn_embed_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_embed_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: fastmnn_embed_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_embed_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: fastmnn_embed_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_feature_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: fastmnn_feature_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_feature_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: fastmnn_feature_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_feature_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: fastmnn_feature_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'fastmnn_feature_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: fastmnn_feature_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'harmony_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: harmony_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'harmony_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: harmony_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'harmony_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: harmony_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'harmony_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: harmony_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'liger_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: liger_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'liger_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: liger_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'mnn_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: mnn_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'mnn_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: mnn_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'mnn_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: mnn_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'mnn_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: mnn_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'no_integration' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: no_integration\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'random_integration' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: random_integration\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'scalex_full' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: scalex_full\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'scalex_hvg' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: scalex_hvg\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_embed_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: scanorama_embed_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_embed_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: scanorama_embed_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_embed_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: scanorama_embed_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_embed_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: scanorama_embed_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_feature_full_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: scanorama_feature_full_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_feature_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: scanorama_feature_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_feature_hvg_scaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: scanorama_feature_hvg_scaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'scanorama_feature_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: scanorama_feature_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'scanvi_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: scanvi_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'scanvi_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: scanvi_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'scvi_full_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: scvi_full_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Method 'scvi_hvg_unscaled' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  method id: scvi_hvg_unscaled\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Dataset 'immune_batch' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  dataset id: immune_batch\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Dataset 'lung_batch' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  dataset id: lung_batch\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Raw results", 
-        "name": "Dataset 'pancreas_batch' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: batch_integration_graph\n  dataset id: pancreas_batch\n  Percentage missing: 0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration ari", 
-        "value": 0.012280237427751785, 
-        "severity": 0, 
-        "severity_value": -0.012280237427751785, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: batch_random_integration\n  Metric id: ari\n  Worst score: 0.012280237427751785%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration ari", 
-        "value": 0.12182281522050027, 
-        "severity": 0, 
-        "severity_value": 0.060911407610250136, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: batch_random_integration\n  Metric id: ari\n  Best score: 0.12182281522050027%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_full_scaled ari", 
-        "value": 0.3866107909940958, 
-        "severity": 0, 
-        "severity_value": -0.3866107909940958, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_scaled\n  Metric id: ari\n  Worst score: 0.3866107909940958%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_full_scaled ari", 
-        "value": 0.8309533650063755, 
-        "severity": 0, 
-        "severity_value": 0.41547668250318776, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_scaled\n  Metric id: ari\n  Best score: 0.8309533650063755%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_full_unscaled ari", 
-        "value": 0.5322719041174299, 
-        "severity": 0, 
-        "severity_value": -0.5322719041174299, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_unscaled\n  Metric id: ari\n  Worst score: 0.5322719041174299%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_full_unscaled ari", 
-        "value": 0.9048276831081068, 
-        "severity": 0, 
-        "severity_value": 0.4524138415540534, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_unscaled\n  Metric id: ari\n  Best score: 0.9048276831081068%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_hvg_scaled ari", 
-        "value": 0.5119998990006843, 
-        "severity": 0, 
-        "severity_value": -0.5119998990006843, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_scaled\n  Metric id: ari\n  Worst score: 0.5119998990006843%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_hvg_scaled ari", 
-        "value": 0.9167654472636905, 
-        "severity": 0, 
-        "severity_value": 0.45838272363184523, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_scaled\n  Metric id: ari\n  Best score: 0.9167654472636905%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_hvg_unscaled ari", 
-        "value": 0.5919068629187063, 
-        "severity": 0, 
-        "severity_value": -0.5919068629187063, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.5919068629187063%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_hvg_unscaled ari", 
-        "value": 0.9512686058928673, 
-        "severity": 0, 
-        "severity_value": 0.47563430294643366, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_unscaled\n  Metric id: ari\n  Best score: 0.9512686058928673%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_graph ari", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_graph performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_graph\n  Metric id: ari\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_graph ari", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_graph performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_graph\n  Metric id: ari\n  Best score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration ari", 
-        "value": 0.2147623918697529, 
-        "severity": 0, 
-        "severity_value": -0.2147623918697529, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_integration\n  Metric id: ari\n  Worst score: 0.2147623918697529%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration ari", 
-        "value": 0.35477421945363874, 
-        "severity": 0, 
-        "severity_value": 0.17738710972681937, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_integration\n  Metric id: ari\n  Best score: 0.35477421945363874%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled ari", 
-        "value": 0.32510600501930276, 
-        "severity": 0, 
-        "severity_value": -0.32510600501930276, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_scaled\n  Metric id: ari\n  Worst score: 0.32510600501930276%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled ari", 
-        "value": 0.7037088974139643, 
-        "severity": 0, 
-        "severity_value": 0.3518544487069821, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_scaled\n  Metric id: ari\n  Best score: 0.7037088974139643%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled ari", 
-        "value": 0.5779585623251622, 
-        "severity": 0, 
-        "severity_value": -0.5779585623251622, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_unscaled\n  Metric id: ari\n  Worst score: 0.5779585623251622%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled ari", 
-        "value": 0.948535896361187, 
-        "severity": 0, 
-        "severity_value": 0.4742679481805935, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_unscaled\n  Metric id: ari\n  Best score: 0.948535896361187%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled ari", 
-        "value": 0.46623101820934326, 
-        "severity": 0, 
-        "severity_value": -0.46623101820934326, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_scaled\n  Metric id: ari\n  Worst score: 0.46623101820934326%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled ari", 
-        "value": 0.9475767881708149, 
-        "severity": 0, 
-        "severity_value": 0.47378839408540746, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_scaled\n  Metric id: ari\n  Best score: 0.9475767881708149%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled ari", 
-        "value": 0.4656049791455594, 
-        "severity": 0, 
-        "severity_value": -0.4656049791455594, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.4656049791455594%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled ari", 
-        "value": 0.9442360641667916, 
-        "severity": 0, 
-        "severity_value": 0.4721180320833958, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_unscaled\n  Metric id: ari\n  Best score: 0.9442360641667916%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_scaled ari", 
-        "value": 0.48537436041170157, 
-        "severity": 0, 
-        "severity_value": -0.48537436041170157, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_scaled\n  Metric id: ari\n  Worst score: 0.48537436041170157%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_scaled ari", 
-        "value": 0.884245145450476, 
-        "severity": 0, 
-        "severity_value": 0.442122572725238, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_scaled\n  Metric id: ari\n  Best score: 0.884245145450476%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_unscaled ari", 
-        "value": 0.4874118371263095, 
-        "severity": 0, 
-        "severity_value": -0.4874118371263095, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: ari\n  Worst score: 0.4874118371263095%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_unscaled ari", 
-        "value": 0.8829155705022084, 
-        "severity": 0, 
-        "severity_value": 0.4414577852511042, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: ari\n  Best score: 0.8829155705022084%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_scaled ari", 
-        "value": 0.621868018642736, 
-        "severity": 0, 
-        "severity_value": -0.621868018642736, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: ari\n  Worst score: 0.621868018642736%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_scaled ari", 
-        "value": 0.8409322847608743, 
-        "severity": 0, 
-        "severity_value": 0.42046614238043717, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: ari\n  Best score: 0.8409322847608743%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_unscaled ari", 
-        "value": 0.6165244250292856, 
-        "severity": 0, 
-        "severity_value": -0.6165244250292856, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.6165244250292856%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_unscaled ari", 
-        "value": 0.9266907175090617, 
-        "severity": 0, 
-        "severity_value": 0.46334535875453087, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: ari\n  Best score: 0.9266907175090617%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled ari", 
-        "value": 0.49092395634136254, 
-        "severity": 0, 
-        "severity_value": -0.49092395634136254, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_scaled\n  Metric id: ari\n  Worst score: 0.49092395634136254%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled ari", 
-        "value": 0.8938642175132702, 
-        "severity": 0, 
-        "severity_value": 0.4469321087566351, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_scaled\n  Metric id: ari\n  Best score: 0.8938642175132702%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled ari", 
-        "value": 0.4940700714540273, 
-        "severity": 0, 
-        "severity_value": -0.4940700714540273, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: ari\n  Worst score: 0.4940700714540273%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled ari", 
-        "value": 0.8910900494097485, 
-        "severity": 0, 
-        "severity_value": 0.4455450247048742, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: ari\n  Best score: 0.8910900494097485%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled ari", 
-        "value": 0.5691724213369562, 
-        "severity": 0, 
-        "severity_value": -0.5691724213369562, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: ari\n  Worst score: 0.5691724213369562%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled ari", 
-        "value": 0.7660002185677557, 
-        "severity": 0, 
-        "severity_value": 0.38300010928387784, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: ari\n  Best score: 0.7660002185677557%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled ari", 
-        "value": 0.6055793584855502, 
-        "severity": 0, 
-        "severity_value": -0.6055793584855502, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.6055793584855502%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled ari", 
-        "value": 0.8409183535015032, 
-        "severity": 0, 
-        "severity_value": 0.4204591767507516, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: ari\n  Best score: 0.8409183535015032%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_scaled ari", 
-        "value": 0.39941805730362295, 
-        "severity": 0, 
-        "severity_value": -0.39941805730362295, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_scaled\n  Metric id: ari\n  Worst score: 0.39941805730362295%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_scaled ari", 
-        "value": 0.8334825513916063, 
-        "severity": 0, 
-        "severity_value": 0.41674127569580316, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_scaled\n  Metric id: ari\n  Best score: 0.8334825513916063%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_unscaled ari", 
-        "value": 0.4606385447932734, 
-        "severity": 0, 
-        "severity_value": -0.4606385447932734, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_unscaled\n  Metric id: ari\n  Worst score: 0.4606385447932734%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_unscaled ari", 
-        "value": 0.9145520953646037, 
-        "severity": 0, 
-        "severity_value": 0.45727604768230184, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_unscaled\n  Metric id: ari\n  Best score: 0.9145520953646037%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_scaled ari", 
-        "value": 0.44812626950551043, 
-        "severity": 0, 
-        "severity_value": -0.44812626950551043, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_scaled\n  Metric id: ari\n  Worst score: 0.44812626950551043%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_scaled ari", 
-        "value": 0.9063371657227808, 
-        "severity": 0, 
-        "severity_value": 0.4531685828613904, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_scaled\n  Metric id: ari\n  Best score: 0.9063371657227808%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_unscaled ari", 
-        "value": 0.5206457204698136, 
-        "severity": 0, 
-        "severity_value": -0.5206457204698136, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.5206457204698136%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_unscaled ari", 
-        "value": 0.9444504388987174, 
-        "severity": 0, 
-        "severity_value": 0.4722252194493587, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_unscaled\n  Metric id: ari\n  Best score: 0.9444504388987174%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score liger_full_unscaled ari", 
-        "value": 0.05144869438489706, 
-        "severity": 0, 
-        "severity_value": -0.05144869438489706, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_full_unscaled\n  Metric id: ari\n  Worst score: 0.05144869438489706%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score liger_full_unscaled ari", 
-        "value": 0.5065288626405062, 
-        "severity": 0, 
-        "severity_value": 0.2532644313202531, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_full_unscaled\n  Metric id: ari\n  Best score: 0.5065288626405062%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score liger_hvg_unscaled ari", 
-        "value": 0.08708900722590814, 
-        "severity": 0, 
-        "severity_value": -0.08708900722590814, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.08708900722590814%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score liger_hvg_unscaled ari", 
-        "value": 0.6951746364996462, 
-        "severity": 0, 
-        "severity_value": 0.3475873182498231, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_hvg_unscaled\n  Metric id: ari\n  Best score: 0.6951746364996462%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled ari", 
-        "value": 0.417009266291116, 
-        "severity": 0, 
-        "severity_value": -0.417009266291116, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_scaled\n  Metric id: ari\n  Worst score: 0.417009266291116%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled ari", 
-        "value": 0.7168230817179951, 
-        "severity": 0, 
-        "severity_value": 0.35841154085899757, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_scaled\n  Metric id: ari\n  Best score: 0.7168230817179951%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled ari", 
-        "value": 0.49189714368640464, 
-        "severity": 0, 
-        "severity_value": -0.49189714368640464, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_unscaled\n  Metric id: ari\n  Worst score: 0.49189714368640464%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled ari", 
-        "value": 0.8418019608384186, 
-        "severity": 0, 
-        "severity_value": 0.4209009804192093, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_unscaled\n  Metric id: ari\n  Best score: 0.8418019608384186%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled ari", 
-        "value": 0.5657741181641581, 
-        "severity": 0, 
-        "severity_value": -0.5657741181641581, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_scaled\n  Metric id: ari\n  Worst score: 0.5657741181641581%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled ari", 
-        "value": 0.9448421322659639, 
-        "severity": 0, 
-        "severity_value": 0.47242106613298196, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_scaled\n  Metric id: ari\n  Best score: 0.9448421322659639%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled ari", 
-        "value": 0.5006679726811925, 
-        "severity": 0, 
-        "severity_value": -0.5006679726811925, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.5006679726811925%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled ari", 
-        "value": 0.806456553325415, 
-        "severity": 0, 
-        "severity_value": 0.4032282766627075, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_unscaled\n  Metric id: ari\n  Best score: 0.806456553325415%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration ari", 
-        "value": 0.22162372603273628, 
-        "severity": 0, 
-        "severity_value": -0.22162372603273628, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: no_integration\n  Metric id: ari\n  Worst score: 0.22162372603273628%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score no_integration ari", 
-        "value": 0.35383284865299824, 
-        "severity": 0, 
-        "severity_value": 0.17691642432649912, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: no_integration\n  Metric id: ari\n  Best score: 0.35383284865299824%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration ari", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: random_integration\n  Metric id: ari\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score random_integration ari", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: random_integration\n  Metric id: ari\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full ari", 
-        "value": 0.5810182412193918, 
-        "severity": 0, 
-        "severity_value": -0.5810182412193918, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_full\n  Metric id: ari\n  Worst score: 0.5810182412193918%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full ari", 
-        "value": 0.9185406653443541, 
-        "severity": 0, 
-        "severity_value": 0.45927033267217704, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_full\n  Metric id: ari\n  Best score: 0.9185406653443541%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg ari", 
-        "value": 0.6058398315096319, 
-        "severity": 0, 
-        "severity_value": -0.6058398315096319, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_hvg\n  Metric id: ari\n  Worst score: 0.6058398315096319%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg ari", 
-        "value": 0.9414895868273879, 
-        "severity": 0, 
-        "severity_value": 0.47074479341369396, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_hvg\n  Metric id: ari\n  Best score: 0.9414895868273879%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_scaled ari", 
-        "value": 0.4550038896226381, 
-        "severity": 0, 
-        "severity_value": -0.4550038896226381, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_scaled\n  Metric id: ari\n  Worst score: 0.4550038896226381%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_scaled ari", 
-        "value": 0.9197718168828396, 
-        "severity": 0, 
-        "severity_value": 0.4598859084414198, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_scaled\n  Metric id: ari\n  Best score: 0.9197718168828396%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_unscaled ari", 
-        "value": 0.47972219173828506, 
-        "severity": 0, 
-        "severity_value": -0.47972219173828506, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_unscaled\n  Metric id: ari\n  Worst score: 0.47972219173828506%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_unscaled ari", 
-        "value": 0.6690860602351496, 
-        "severity": 0, 
-        "severity_value": 0.3345430301175748, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_unscaled\n  Metric id: ari\n  Best score: 0.6690860602351496%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_scaled ari", 
-        "value": 0.5560709155760082, 
-        "severity": 0, 
-        "severity_value": -0.5560709155760082, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: ari\n  Worst score: 0.5560709155760082%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_scaled ari", 
-        "value": 0.9523806404534549, 
-        "severity": 0, 
-        "severity_value": 0.47619032022672747, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: ari\n  Best score: 0.9523806404534549%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_unscaled ari", 
-        "value": 0.5074834888127979, 
-        "severity": 0, 
-        "severity_value": -0.5074834888127979, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.5074834888127979%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_unscaled ari", 
-        "value": 0.9578039150918454, 
-        "severity": 0, 
-        "severity_value": 0.4789019575459227, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: ari\n  Best score: 0.9578039150918454%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled ari", 
-        "value": 0.4326518090502041, 
-        "severity": 0, 
-        "severity_value": -0.4326518090502041, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_scaled\n  Metric id: ari\n  Worst score: 0.4326518090502041%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled ari", 
-        "value": 0.6978823097669848, 
-        "severity": 0, 
-        "severity_value": 0.3489411548834924, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_scaled\n  Metric id: ari\n  Best score: 0.6978823097669848%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled ari", 
-        "value": 0.4450467679575944, 
-        "severity": 0, 
-        "severity_value": -0.4450467679575944, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_unscaled\n  Metric id: ari\n  Worst score: 0.4450467679575944%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled ari", 
-        "value": 0.6094260818356965, 
-        "severity": 0, 
-        "severity_value": 0.30471304091784823, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_unscaled\n  Metric id: ari\n  Best score: 0.6094260818356965%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled ari", 
-        "value": 0.4835951850201288, 
-        "severity": 0, 
-        "severity_value": -0.4835951850201288, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: ari\n  Worst score: 0.4835951850201288%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled ari", 
-        "value": 0.943334873569761, 
-        "severity": 0, 
-        "severity_value": 0.4716674367848805, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: ari\n  Best score: 0.943334873569761%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled ari", 
-        "value": 0.5295121495332352, 
-        "severity": 0, 
-        "severity_value": -0.5295121495332352, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.5295121495332352%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled ari", 
-        "value": 0.7830414122912888, 
-        "severity": 0, 
-        "severity_value": 0.3915207061456444, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: ari\n  Best score: 0.7830414122912888%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_full_unscaled ari", 
-        "value": 0.7044395363736535, 
-        "severity": 0, 
-        "severity_value": -0.7044395363736535, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_full_unscaled\n  Metric id: ari\n  Worst score: 0.7044395363736535%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_full_unscaled ari", 
-        "value": 0.9485386394724674, 
-        "severity": 0, 
-        "severity_value": 0.4742693197362337, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_full_unscaled\n  Metric id: ari\n  Best score: 0.9485386394724674%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_hvg_unscaled ari", 
-        "value": 0.7717042041795847, 
-        "severity": 0, 
-        "severity_value": -0.7717042041795847, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.7717042041795847%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_hvg_unscaled ari", 
-        "value": 0.9536167634483413, 
-        "severity": 0, 
-        "severity_value": 0.47680838172417067, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_hvg_unscaled\n  Metric id: ari\n  Best score: 0.9536167634483413%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_full_unscaled ari", 
-        "value": 0.5889625968697515, 
-        "severity": 0, 
-        "severity_value": -0.5889625968697515, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_full_unscaled\n  Metric id: ari\n  Worst score: 0.5889625968697515%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scvi_full_unscaled ari", 
-        "value": 0.9447670776095792, 
-        "severity": 0, 
-        "severity_value": 0.4723835388047896, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_full_unscaled\n  Metric id: ari\n  Best score: 0.9447670776095792%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_hvg_unscaled ari", 
-        "value": 0.5770546864191692, 
-        "severity": 0, 
-        "severity_value": -0.5770546864191692, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_hvg_unscaled\n  Metric id: ari\n  Worst score: 0.5770546864191692%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scvi_hvg_unscaled ari", 
-        "value": 0.9495815268557484, 
-        "severity": 0, 
-        "severity_value": 0.4747907634278742, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_hvg_unscaled\n  Metric id: ari\n  Best score: 0.9495815268557484%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration graph_connectivity", 
-        "value": 0.07158483173200725, 
-        "severity": 0, 
-        "severity_value": -0.07158483173200725, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: batch_random_integration\n  Metric id: graph_connectivity\n  Worst score: 0.07158483173200725%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration graph_connectivity", 
-        "value": 0.4396605593238685, 
-        "severity": 0, 
-        "severity_value": 0.21983027966193425, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: batch_random_integration\n  Metric id: graph_connectivity\n  Best score: 0.4396605593238685%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_full_scaled graph_connectivity", 
-        "value": 0.9496730836872557, 
-        "severity": 0, 
-        "severity_value": -0.9496730836872557, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9496730836872557%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_full_scaled graph_connectivity", 
-        "value": 0.9965596113991056, 
-        "severity": 0, 
-        "severity_value": 0.4982798056995528, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9965596113991056%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_full_unscaled graph_connectivity", 
-        "value": 0.9879268044285299, 
-        "severity": 0, 
-        "severity_value": -0.9879268044285299, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9879268044285299%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_full_unscaled graph_connectivity", 
-        "value": 0.9949547561179379, 
-        "severity": 0, 
-        "severity_value": 0.49747737805896897, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9949547561179379%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_hvg_scaled graph_connectivity", 
-        "value": 0.9854263712970512, 
-        "severity": 0, 
-        "severity_value": -0.9854263712970512, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9854263712970512%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_hvg_scaled graph_connectivity", 
-        "value": 0.9969412954039657, 
-        "severity": 0, 
-        "severity_value": 0.49847064770198285, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9969412954039657%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_hvg_unscaled graph_connectivity", 
-        "value": 0.979341110466939, 
-        "severity": 0, 
-        "severity_value": -0.979341110466939, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.979341110466939%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_hvg_unscaled graph_connectivity", 
-        "value": 0.9920796586758223, 
-        "severity": 0, 
-        "severity_value": 0.49603982933791113, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9920796586758223%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_graph graph_connectivity", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_graph performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_graph\n  Metric id: graph_connectivity\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_graph graph_connectivity", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_graph performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_graph\n  Metric id: graph_connectivity\n  Best score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration graph_connectivity", 
-        "value": 0.7770499989604509, 
-        "severity": 0, 
-        "severity_value": -0.7770499989604509, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_integration\n  Metric id: graph_connectivity\n  Worst score: 0.7770499989604509%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration graph_connectivity", 
-        "value": 0.7919976954872772, 
-        "severity": 0, 
-        "severity_value": 0.3959988477436386, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_integration\n  Metric id: graph_connectivity\n  Best score: 0.7919976954872772%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled graph_connectivity", 
-        "value": 0.9304897107502157, 
-        "severity": 0, 
-        "severity_value": -0.9304897107502157, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9304897107502157%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled graph_connectivity", 
-        "value": 0.9753945549979929, 
-        "severity": 0, 
-        "severity_value": 0.48769727749899644, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9753945549979929%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled graph_connectivity", 
-        "value": 0.9342259587350628, 
-        "severity": 0, 
-        "severity_value": -0.9342259587350628, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9342259587350628%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled graph_connectivity", 
-        "value": 0.9920137244606215, 
-        "severity": 0, 
-        "severity_value": 0.49600686223031076, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9920137244606215%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled graph_connectivity", 
-        "value": 0.9294086611120054, 
-        "severity": 0, 
-        "severity_value": -0.9294086611120054, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9294086611120054%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled graph_connectivity", 
-        "value": 0.9939882828595885, 
-        "severity": 0, 
-        "severity_value": 0.49699414142979426, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9939882828595885%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled graph_connectivity", 
-        "value": 0.9475504455122941, 
-        "severity": 0, 
-        "severity_value": -0.9475504455122941, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9475504455122941%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled graph_connectivity", 
-        "value": 0.9937089485797244, 
-        "severity": 0, 
-        "severity_value": 0.4968544742898622, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9937089485797244%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_scaled graph_connectivity", 
-        "value": 0.9465264335668649, 
-        "severity": 0, 
-        "severity_value": -0.9465264335668649, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9465264335668649%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_scaled graph_connectivity", 
-        "value": 0.9728201413696858, 
-        "severity": 0, 
-        "severity_value": 0.4864100706848429, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9728201413696858%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_unscaled graph_connectivity", 
-        "value": 0.9466255759925416, 
-        "severity": 0, 
-        "severity_value": -0.9466255759925416, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9466255759925416%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_unscaled graph_connectivity", 
-        "value": 0.9724072097466546, 
-        "severity": 0, 
-        "severity_value": 0.4862036048733273, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9724072097466546%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_scaled graph_connectivity", 
-        "value": 0.9404042036121514, 
-        "severity": 0, 
-        "severity_value": -0.9404042036121514, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9404042036121514%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_scaled graph_connectivity", 
-        "value": 0.9742134218059894, 
-        "severity": 0, 
-        "severity_value": 0.4871067109029947, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9742134218059894%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_unscaled graph_connectivity", 
-        "value": 0.9426298588211063, 
-        "severity": 0, 
-        "severity_value": -0.9426298588211063, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9426298588211063%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_unscaled graph_connectivity", 
-        "value": 0.9733961907786022, 
-        "severity": 0, 
-        "severity_value": 0.4866980953893011, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9733961907786022%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled graph_connectivity", 
-        "value": 0.9340384258930982, 
-        "severity": 0, 
-        "severity_value": -0.9340384258930982, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9340384258930982%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled graph_connectivity", 
-        "value": 0.9720791426861347, 
-        "severity": 0, 
-        "severity_value": 0.48603957134306736, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9720791426861347%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled graph_connectivity", 
-        "value": 0.9351400993437342, 
-        "severity": 0, 
-        "severity_value": -0.9351400993437342, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9351400993437342%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled graph_connectivity", 
-        "value": 0.9718996104162081, 
-        "severity": 0, 
-        "severity_value": 0.48594980520810405, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9718996104162081%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled graph_connectivity", 
-        "value": 0.9379144011656445, 
-        "severity": 0, 
-        "severity_value": -0.9379144011656445, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9379144011656445%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled graph_connectivity", 
-        "value": 0.9736822988516873, 
-        "severity": 0, 
-        "severity_value": 0.48684114942584367, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9736822988516873%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled graph_connectivity", 
-        "value": 0.940843431976507, 
-        "severity": 0, 
-        "severity_value": -0.940843431976507, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.940843431976507%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled graph_connectivity", 
-        "value": 0.9737227752782032, 
-        "severity": 0, 
-        "severity_value": 0.4868613876391016, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9737227752782032%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_scaled graph_connectivity", 
-        "value": 0.9074913611089288, 
-        "severity": 0, 
-        "severity_value": -0.9074913611089288, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9074913611089288%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_scaled graph_connectivity", 
-        "value": 0.9837928907473997, 
-        "severity": 0, 
-        "severity_value": 0.49189644537369986, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9837928907473997%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_unscaled graph_connectivity", 
-        "value": 0.9142985744866094, 
-        "severity": 0, 
-        "severity_value": -0.9142985744866094, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9142985744866094%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_unscaled graph_connectivity", 
-        "value": 0.989464387360835, 
-        "severity": 0, 
-        "severity_value": 0.4947321936804175, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.989464387360835%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_scaled graph_connectivity", 
-        "value": 0.8989211795583301, 
-        "severity": 0, 
-        "severity_value": -0.8989211795583301, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.8989211795583301%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_scaled graph_connectivity", 
-        "value": 0.9820570849948859, 
-        "severity": 0, 
-        "severity_value": 0.49102854249744293, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9820570849948859%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_unscaled graph_connectivity", 
-        "value": 0.93032158555295, 
-        "severity": 0, 
-        "severity_value": -0.93032158555295, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.93032158555295%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_unscaled graph_connectivity", 
-        "value": 0.9882803996766686, 
-        "severity": 0, 
-        "severity_value": 0.4941401998383343, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9882803996766686%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score liger_full_unscaled graph_connectivity", 
-        "value": 0.4546069715395459, 
-        "severity": 0, 
-        "severity_value": -0.4546069715395459, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.4546069715395459%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score liger_full_unscaled graph_connectivity", 
-        "value": 0.8735758757317643, 
-        "severity": 0, 
-        "severity_value": 0.43678793786588216, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.8735758757317643%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score liger_hvg_unscaled graph_connectivity", 
-        "value": 0.3390146246443806, 
-        "severity": 0, 
-        "severity_value": -0.3390146246443806, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.3390146246443806%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score liger_hvg_unscaled graph_connectivity", 
-        "value": 0.8664772541418317, 
-        "severity": 0, 
-        "severity_value": 0.43323862707091587, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.8664772541418317%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled graph_connectivity", 
-        "value": 0.9601747693488036, 
-        "severity": 0, 
-        "severity_value": -0.9601747693488036, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9601747693488036%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled graph_connectivity", 
-        "value": 0.9775227361287484, 
-        "severity": 0, 
-        "severity_value": 0.4887613680643742, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9775227361287484%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled graph_connectivity", 
-        "value": 0.97689723177387, 
-        "severity": 0, 
-        "severity_value": -0.97689723177387, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.97689723177387%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled graph_connectivity", 
-        "value": 0.98556408520618, 
-        "severity": 0, 
-        "severity_value": 0.49278204260309, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.98556408520618%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled graph_connectivity", 
-        "value": 0.9718021153065958, 
-        "severity": 0, 
-        "severity_value": -0.9718021153065958, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9718021153065958%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled graph_connectivity", 
-        "value": 0.9930497127725678, 
-        "severity": 0, 
-        "severity_value": 0.4965248563862839, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9930497127725678%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled graph_connectivity", 
-        "value": 0.9787981098126087, 
-        "severity": 0, 
-        "severity_value": -0.9787981098126087, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9787981098126087%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled graph_connectivity", 
-        "value": 0.9940790634173758, 
-        "severity": 0, 
-        "severity_value": 0.4970395317086879, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9940790634173758%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration graph_connectivity", 
-        "value": 0.7770499989604509, 
-        "severity": 0, 
-        "severity_value": -0.7770499989604509, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: no_integration\n  Metric id: graph_connectivity\n  Worst score: 0.7770499989604509%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score no_integration graph_connectivity", 
-        "value": 0.7919976954872772, 
-        "severity": 0, 
-        "severity_value": 0.3959988477436386, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: no_integration\n  Metric id: graph_connectivity\n  Best score: 0.7919976954872772%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration graph_connectivity", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: random_integration\n  Metric id: graph_connectivity\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score random_integration graph_connectivity", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: random_integration\n  Metric id: graph_connectivity\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full graph_connectivity", 
-        "value": 0.9554643936889265, 
-        "severity": 0, 
-        "severity_value": -0.9554643936889265, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_full\n  Metric id: graph_connectivity\n  Worst score: 0.9554643936889265%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full graph_connectivity", 
-        "value": 0.9875749056755274, 
-        "severity": 0, 
-        "severity_value": 0.4937874528377637, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_full\n  Metric id: graph_connectivity\n  Best score: 0.9875749056755274%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg graph_connectivity", 
-        "value": 0.9617907283281844, 
-        "severity": 0, 
-        "severity_value": -0.9617907283281844, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_hvg\n  Metric id: graph_connectivity\n  Worst score: 0.9617907283281844%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg graph_connectivity", 
-        "value": 0.993271335367633, 
-        "severity": 0, 
-        "severity_value": 0.4966356676838165, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_hvg\n  Metric id: graph_connectivity\n  Best score: 0.993271335367633%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_scaled graph_connectivity", 
-        "value": 0.9492473708659833, 
-        "severity": 0, 
-        "severity_value": -0.9492473708659833, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9492473708659833%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_scaled graph_connectivity", 
-        "value": 0.9911150187916778, 
-        "severity": 0, 
-        "severity_value": 0.4955575093958389, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9911150187916778%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_unscaled graph_connectivity", 
-        "value": 0.8171672249621622, 
-        "severity": 0, 
-        "severity_value": -0.8171672249621622, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.8171672249621622%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_unscaled graph_connectivity", 
-        "value": 0.9893041278611009, 
-        "severity": 0, 
-        "severity_value": 0.49465206393055045, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9893041278611009%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_scaled graph_connectivity", 
-        "value": 0.9455034333898866, 
-        "severity": 0, 
-        "severity_value": -0.9455034333898866, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.9455034333898866%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_scaled graph_connectivity", 
-        "value": 0.9919802948069835, 
-        "severity": 0, 
-        "severity_value": 0.49599014740349173, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9919802948069835%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_unscaled graph_connectivity", 
-        "value": 0.8576720658651062, 
-        "severity": 0, 
-        "severity_value": -0.8576720658651062, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.8576720658651062%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_unscaled graph_connectivity", 
-        "value": 0.9937862416927106, 
-        "severity": 0, 
-        "severity_value": 0.4968931208463553, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9937862416927106%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled graph_connectivity", 
-        "value": 0.8687217580683755, 
-        "severity": 0, 
-        "severity_value": -0.8687217580683755, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.8687217580683755%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled graph_connectivity", 
-        "value": 0.9731712233596375, 
-        "severity": 0, 
-        "severity_value": 0.48658561167981873, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9731712233596375%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled graph_connectivity", 
-        "value": 0.7687907095180788, 
-        "severity": 0, 
-        "severity_value": -0.7687907095180788, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.7687907095180788%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled graph_connectivity", 
-        "value": 0.9894625005075038, 
-        "severity": 0, 
-        "severity_value": 0.4947312502537519, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9894625005075038%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled graph_connectivity", 
-        "value": 0.8489748583290194, 
-        "severity": 0, 
-        "severity_value": -0.8489748583290194, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: graph_connectivity\n  Worst score: 0.8489748583290194%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled graph_connectivity", 
-        "value": 0.9820865851512731, 
-        "severity": 0, 
-        "severity_value": 0.49104329257563656, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: graph_connectivity\n  Best score: 0.9820865851512731%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled graph_connectivity", 
-        "value": 0.8143281502088751, 
-        "severity": 0, 
-        "severity_value": -0.8143281502088751, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.8143281502088751%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled graph_connectivity", 
-        "value": 0.9904602090322913, 
-        "severity": 0, 
-        "severity_value": 0.49523010451614563, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9904602090322913%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_full_unscaled graph_connectivity", 
-        "value": 0.982415070583696, 
-        "severity": 0, 
-        "severity_value": -0.982415070583696, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.982415070583696%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_full_unscaled graph_connectivity", 
-        "value": 0.9950658062473096, 
-        "severity": 0, 
-        "severity_value": 0.4975329031236548, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9950658062473096%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_hvg_unscaled graph_connectivity", 
-        "value": 0.9784003094916958, 
-        "severity": 0, 
-        "severity_value": -0.9784003094916958, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9784003094916958%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_hvg_unscaled graph_connectivity", 
-        "value": 0.9947018510370409, 
-        "severity": 0, 
-        "severity_value": 0.49735092551852045, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9947018510370409%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_full_unscaled graph_connectivity", 
-        "value": 0.9786605137682096, 
-        "severity": 0, 
-        "severity_value": -0.9786605137682096, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_full_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.9786605137682096%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scvi_full_unscaled graph_connectivity", 
-        "value": 0.9958282786748562, 
-        "severity": 0, 
-        "severity_value": 0.4979141393374281, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_full_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9958282786748562%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_hvg_unscaled graph_connectivity", 
-        "value": 0.98012811093542, 
-        "severity": 0, 
-        "severity_value": -0.98012811093542, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_hvg_unscaled\n  Metric id: graph_connectivity\n  Worst score: 0.98012811093542%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scvi_hvg_unscaled graph_connectivity", 
-        "value": 0.9965798254595216, 
-        "severity": 0, 
-        "severity_value": 0.4982899127297608, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_hvg_unscaled\n  Metric id: graph_connectivity\n  Best score: 0.9965798254595216%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration isolated_labels_f1", 
-        "value": 0.031207780650650666, 
-        "severity": 0, 
-        "severity_value": -0.031207780650650666, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: batch_random_integration\n  Metric id: isolated_labels_f1\n  Worst score: 0.031207780650650666%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration isolated_labels_f1", 
-        "value": 0.09595417712552194, 
-        "severity": 0, 
-        "severity_value": 0.04797708856276097, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: batch_random_integration\n  Metric id: isolated_labels_f1\n  Best score: 0.09595417712552194%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_full_scaled isolated_labels_f1", 
-        "value": 0.8128056653245341, 
-        "severity": 0, 
-        "severity_value": -0.8128056653245341, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8128056653245341%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_full_scaled isolated_labels_f1", 
-        "value": 0.9518524520360704, 
-        "severity": 0, 
-        "severity_value": 0.4759262260180352, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9518524520360704%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_full_unscaled isolated_labels_f1", 
-        "value": 0.7728089953354313, 
-        "severity": 0, 
-        "severity_value": -0.7728089953354313, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7728089953354313%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_full_unscaled isolated_labels_f1", 
-        "value": 0.8849354401483805, 
-        "severity": 0, 
-        "severity_value": 0.4424677200741903, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8849354401483805%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_hvg_scaled isolated_labels_f1", 
-        "value": 0.7793801228122426, 
-        "severity": 0, 
-        "severity_value": -0.7793801228122426, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7793801228122426%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_hvg_scaled isolated_labels_f1", 
-        "value": 0.9411677959496868, 
-        "severity": 0, 
-        "severity_value": 0.4705838979748434, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9411677959496868%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_hvg_unscaled isolated_labels_f1", 
-        "value": 0.8139675127075139, 
-        "severity": 0, 
-        "severity_value": -0.8139675127075139, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8139675127075139%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_hvg_unscaled isolated_labels_f1", 
-        "value": 0.8756865971002332, 
-        "severity": 0, 
-        "severity_value": 0.4378432985501166, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8756865971002332%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_graph isolated_labels_f1", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_graph performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_graph\n  Metric id: isolated_labels_f1\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_graph isolated_labels_f1", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_graph performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_graph\n  Metric id: isolated_labels_f1\n  Best score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration isolated_labels_f1", 
-        "value": 0.6952655841901478, 
-        "severity": 0, 
-        "severity_value": -0.6952655841901478, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_integration\n  Metric id: isolated_labels_f1\n  Worst score: 0.6952655841901478%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration isolated_labels_f1", 
-        "value": 0.7934311491724283, 
-        "severity": 0, 
-        "severity_value": 0.39671557458621415, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_integration\n  Metric id: isolated_labels_f1\n  Best score: 0.7934311491724283%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled isolated_labels_f1", 
-        "value": 0.22178185655910096, 
-        "severity": 0, 
-        "severity_value": -0.22178185655910096, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.22178185655910096%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled isolated_labels_f1", 
-        "value": 0.7327188357589154, 
-        "severity": 0, 
-        "severity_value": 0.3663594178794577, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.7327188357589154%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled isolated_labels_f1", 
-        "value": 0.7076273775098766, 
-        "severity": 0, 
-        "severity_value": -0.7076273775098766, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7076273775098766%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled isolated_labels_f1", 
-        "value": 0.949434991059844, 
-        "severity": 0, 
-        "severity_value": 0.474717495529922, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.949434991059844%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled isolated_labels_f1", 
-        "value": 0.7012198199289406, 
-        "severity": 0, 
-        "severity_value": -0.7012198199289406, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7012198199289406%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled isolated_labels_f1", 
-        "value": 0.9545919453738935, 
-        "severity": 0, 
-        "severity_value": 0.47729597268694673, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9545919453738935%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled isolated_labels_f1", 
-        "value": 0.7177895677541293, 
-        "severity": 0, 
-        "severity_value": -0.7177895677541293, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7177895677541293%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled isolated_labels_f1", 
-        "value": 0.925551079729459, 
-        "severity": 0, 
-        "severity_value": 0.4627755398647295, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.925551079729459%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_scaled isolated_labels_f1", 
-        "value": 0.6323015613058194, 
-        "severity": 0, 
-        "severity_value": -0.6323015613058194, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.6323015613058194%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_scaled isolated_labels_f1", 
-        "value": 0.7740848126030386, 
-        "severity": 0, 
-        "severity_value": 0.3870424063015193, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.7740848126030386%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_unscaled isolated_labels_f1", 
-        "value": 0.6323864409084728, 
-        "severity": 0, 
-        "severity_value": -0.6323864409084728, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.6323864409084728%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_unscaled isolated_labels_f1", 
-        "value": 0.7892147188049101, 
-        "severity": 0, 
-        "severity_value": 0.39460735940245506, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.7892147188049101%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_scaled isolated_labels_f1", 
-        "value": 0.6627170978993757, 
-        "severity": 0, 
-        "severity_value": -0.6627170978993757, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.6627170978993757%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_scaled isolated_labels_f1", 
-        "value": 0.8449971756063042, 
-        "severity": 0, 
-        "severity_value": 0.4224985878031521, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8449971756063042%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_unscaled isolated_labels_f1", 
-        "value": 0.6553166821618196, 
-        "severity": 0, 
-        "severity_value": -0.6553166821618196, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.6553166821618196%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_unscaled isolated_labels_f1", 
-        "value": 0.846355888122564, 
-        "severity": 0, 
-        "severity_value": 0.423177944061282, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.846355888122564%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled isolated_labels_f1", 
-        "value": 0.642150111197384, 
-        "severity": 0, 
-        "severity_value": -0.642150111197384, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.642150111197384%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled isolated_labels_f1", 
-        "value": 0.7484549533052555, 
-        "severity": 0, 
-        "severity_value": 0.37422747665262773, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.7484549533052555%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled isolated_labels_f1", 
-        "value": 0.6400991163301015, 
-        "severity": 0, 
-        "severity_value": -0.6400991163301015, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.6400991163301015%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled isolated_labels_f1", 
-        "value": 0.7707019865580206, 
-        "severity": 0, 
-        "severity_value": 0.3853509932790103, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.7707019865580206%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled isolated_labels_f1", 
-        "value": 0.6930958984570159, 
-        "severity": 0, 
-        "severity_value": -0.6930958984570159, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.6930958984570159%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled isolated_labels_f1", 
-        "value": 0.8435165189382197, 
-        "severity": 0, 
-        "severity_value": 0.42175825946910983, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8435165189382197%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled isolated_labels_f1", 
-        "value": 0.6608631905062242, 
-        "severity": 0, 
-        "severity_value": -0.6608631905062242, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.6608631905062242%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled isolated_labels_f1", 
-        "value": 0.8446333840017965, 
-        "severity": 0, 
-        "severity_value": 0.4223166920008983, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8446333840017965%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_scaled isolated_labels_f1", 
-        "value": 0.33351430840634383, 
-        "severity": 0, 
-        "severity_value": -0.33351430840634383, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.33351430840634383%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_scaled isolated_labels_f1", 
-        "value": 0.9153566200704916, 
-        "severity": 0, 
-        "severity_value": 0.4576783100352458, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9153566200704916%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_unscaled isolated_labels_f1", 
-        "value": 0.7339376107458243, 
-        "severity": 0, 
-        "severity_value": -0.7339376107458243, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7339376107458243%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_unscaled isolated_labels_f1", 
-        "value": 0.8677536741933669, 
-        "severity": 0, 
-        "severity_value": 0.43387683709668345, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8677536741933669%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_scaled isolated_labels_f1", 
-        "value": 0.29312765293052884, 
-        "severity": 0, 
-        "severity_value": -0.29312765293052884, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.29312765293052884%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_scaled isolated_labels_f1", 
-        "value": 0.8024548513367078, 
-        "severity": 0, 
-        "severity_value": 0.4012274256683539, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8024548513367078%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_unscaled isolated_labels_f1", 
-        "value": 0.6719686012345397, 
-        "severity": 0, 
-        "severity_value": -0.6719686012345397, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.6719686012345397%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_unscaled isolated_labels_f1", 
-        "value": 0.9229656536593986, 
-        "severity": 0, 
-        "severity_value": 0.4614828268296993, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9229656536593986%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score liger_full_unscaled isolated_labels_f1", 
-        "value": 0.13937382149591448, 
-        "severity": 0, 
-        "severity_value": -0.13937382149591448, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.13937382149591448%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score liger_full_unscaled isolated_labels_f1", 
-        "value": 0.5753006170345304, 
-        "severity": 0, 
-        "severity_value": 0.2876503085172652, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.5753006170345304%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score liger_hvg_unscaled isolated_labels_f1", 
-        "value": 0.10558358929354977, 
-        "severity": 0, 
-        "severity_value": -0.10558358929354977, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.10558358929354977%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score liger_hvg_unscaled isolated_labels_f1", 
-        "value": 0.6115839002647353, 
-        "severity": 0, 
-        "severity_value": 0.30579195013236765, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.6115839002647353%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled isolated_labels_f1", 
-        "value": 0.3319065333070556, 
-        "severity": 0, 
-        "severity_value": -0.3319065333070556, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.3319065333070556%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled isolated_labels_f1", 
-        "value": 0.83368328282816, 
-        "severity": 0, 
-        "severity_value": 0.41684164141408, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.83368328282816%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled isolated_labels_f1", 
-        "value": 0.7053032962947665, 
-        "severity": 0, 
-        "severity_value": -0.7053032962947665, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7053032962947665%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled isolated_labels_f1", 
-        "value": 0.8840847689065854, 
-        "severity": 0, 
-        "severity_value": 0.4420423844532927, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8840847689065854%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled isolated_labels_f1", 
-        "value": 0.6270869152112931, 
-        "severity": 0, 
-        "severity_value": -0.6270869152112931, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.6270869152112931%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled isolated_labels_f1", 
-        "value": 0.9521306919494714, 
-        "severity": 0, 
-        "severity_value": 0.4760653459747357, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9521306919494714%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled isolated_labels_f1", 
-        "value": 0.7246977583746117, 
-        "severity": 0, 
-        "severity_value": -0.7246977583746117, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7246977583746117%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled isolated_labels_f1", 
-        "value": 0.9261655598303302, 
-        "severity": 0, 
-        "severity_value": 0.4630827799151651, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9261655598303302%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration isolated_labels_f1", 
-        "value": 0.7105510353607755, 
-        "severity": 0, 
-        "severity_value": -0.7105510353607755, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: no_integration\n  Metric id: isolated_labels_f1\n  Worst score: 0.7105510353607755%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score no_integration isolated_labels_f1", 
-        "value": 0.7775522487450057, 
-        "severity": 0, 
-        "severity_value": 0.38877612437250286, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: no_integration\n  Metric id: isolated_labels_f1\n  Best score: 0.7775522487450057%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration isolated_labels_f1", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: random_integration\n  Metric id: isolated_labels_f1\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score random_integration isolated_labels_f1", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: random_integration\n  Metric id: isolated_labels_f1\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full isolated_labels_f1", 
-        "value": 0.1755813953488372, 
-        "severity": 0, 
-        "severity_value": -0.1755813953488372, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_full\n  Metric id: isolated_labels_f1\n  Worst score: 0.1755813953488372%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full isolated_labels_f1", 
-        "value": 0.7619852072036424, 
-        "severity": 0, 
-        "severity_value": 0.3809926036018212, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_full\n  Metric id: isolated_labels_f1\n  Best score: 0.7619852072036424%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg isolated_labels_f1", 
-        "value": 0.1527399005034764, 
-        "severity": 0, 
-        "severity_value": -0.1527399005034764, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_hvg\n  Metric id: isolated_labels_f1\n  Worst score: 0.1527399005034764%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg isolated_labels_f1", 
-        "value": 0.8229229806258218, 
-        "severity": 0, 
-        "severity_value": 0.4114614903129109, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_hvg\n  Metric id: isolated_labels_f1\n  Best score: 0.8229229806258218%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_scaled isolated_labels_f1", 
-        "value": 0.840367113538235, 
-        "severity": 0, 
-        "severity_value": -0.840367113538235, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.840367113538235%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_scaled isolated_labels_f1", 
-        "value": 0.9554625343922197, 
-        "severity": 0, 
-        "severity_value": 0.47773126719610987, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9554625343922197%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_unscaled isolated_labels_f1", 
-        "value": 0.8535697438892642, 
-        "severity": 0, 
-        "severity_value": -0.8535697438892642, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8535697438892642%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_unscaled isolated_labels_f1", 
-        "value": 0.8655839231547017, 
-        "severity": 0, 
-        "severity_value": 0.43279196157735084, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8655839231547017%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_scaled isolated_labels_f1", 
-        "value": 0.8280525002134782, 
-        "severity": 0, 
-        "severity_value": -0.8280525002134782, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8280525002134782%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_scaled isolated_labels_f1", 
-        "value": 0.952341388092852, 
-        "severity": 0, 
-        "severity_value": 0.476170694046426, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.952341388092852%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_unscaled isolated_labels_f1", 
-        "value": 0.7334530012284494, 
-        "severity": 0, 
-        "severity_value": -0.7334530012284494, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7334530012284494%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_unscaled isolated_labels_f1", 
-        "value": 0.9513379261215879, 
-        "severity": 0, 
-        "severity_value": 0.4756689630607939, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9513379261215879%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled isolated_labels_f1", 
-        "value": 0.8026338880968231, 
-        "severity": 0, 
-        "severity_value": -0.8026338880968231, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8026338880968231%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled isolated_labels_f1", 
-        "value": 0.9154103176851115, 
-        "severity": 0, 
-        "severity_value": 0.45770515884255575, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9154103176851115%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled isolated_labels_f1", 
-        "value": 0.7138474443928586, 
-        "severity": 0, 
-        "severity_value": -0.7138474443928586, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7138474443928586%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled isolated_labels_f1", 
-        "value": 0.8516020966400164, 
-        "severity": 0, 
-        "severity_value": 0.4258010483200082, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.8516020966400164%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled isolated_labels_f1", 
-        "value": 0.8368997781138129, 
-        "severity": 0, 
-        "severity_value": -0.8368997781138129, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8368997781138129%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled isolated_labels_f1", 
-        "value": 0.9426395272101262, 
-        "severity": 0, 
-        "severity_value": 0.4713197636050631, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9426395272101262%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled isolated_labels_f1", 
-        "value": 0.7708846274369362, 
-        "severity": 0, 
-        "severity_value": -0.7708846274369362, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7708846274369362%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled isolated_labels_f1", 
-        "value": 0.9172219976344652, 
-        "severity": 0, 
-        "severity_value": 0.4586109988172326, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9172219976344652%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_full_unscaled isolated_labels_f1", 
-        "value": 0.7635733981873923, 
-        "severity": 0, 
-        "severity_value": -0.7635733981873923, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.7635733981873923%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_full_unscaled isolated_labels_f1", 
-        "value": 0.9408618755811935, 
-        "severity": 0, 
-        "severity_value": 0.47043093779059675, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9408618755811935%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_hvg_unscaled isolated_labels_f1", 
-        "value": 0.8495750367330395, 
-        "severity": 0, 
-        "severity_value": -0.8495750367330395, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8495750367330395%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_hvg_unscaled isolated_labels_f1", 
-        "value": 0.9508332643001436, 
-        "severity": 0, 
-        "severity_value": 0.4754166321500718, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9508332643001436%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_full_unscaled isolated_labels_f1", 
-        "value": 0.8109195076889786, 
-        "severity": 0, 
-        "severity_value": -0.8109195076889786, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_full_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8109195076889786%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scvi_full_unscaled isolated_labels_f1", 
-        "value": 0.9386291514179043, 
-        "severity": 0, 
-        "severity_value": 0.46931457570895213, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_full_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9386291514179043%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_hvg_unscaled isolated_labels_f1", 
-        "value": 0.8210202328090113, 
-        "severity": 0, 
-        "severity_value": -0.8210202328090113, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Worst score: 0.8210202328090113%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scvi_hvg_unscaled isolated_labels_f1", 
-        "value": 0.9327717798273036, 
-        "severity": 0, 
-        "severity_value": 0.4663858899136518, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_hvg_unscaled\n  Metric id: isolated_labels_f1\n  Best score: 0.9327717798273036%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score batch_random_integration nmi", 
-        "value": 0.04625926408989999, 
-        "severity": 0, 
-        "severity_value": -0.04625926408989999, 
-        "code": "worst_score >= -1", 
-        "message": "Method batch_random_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: batch_random_integration\n  Metric id: nmi\n  Worst score: 0.04625926408989999%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score batch_random_integration nmi", 
-        "value": 0.3024875711034322, 
-        "severity": 0, 
-        "severity_value": 0.1512437855517161, 
-        "code": "best_score <= 2", 
-        "message": "Method batch_random_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: batch_random_integration\n  Metric id: nmi\n  Best score: 0.3024875711034322%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_full_scaled nmi", 
-        "value": 0.5469209307436801, 
-        "severity": 0, 
-        "severity_value": -0.5469209307436801, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_scaled\n  Metric id: nmi\n  Worst score: 0.5469209307436801%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_full_scaled nmi", 
-        "value": 0.7736224103640912, 
-        "severity": 0, 
-        "severity_value": 0.3868112051820456, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_scaled\n  Metric id: nmi\n  Best score: 0.7736224103640912%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_full_unscaled nmi", 
-        "value": 0.6876227462352709, 
-        "severity": 0, 
-        "severity_value": -0.6876227462352709, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_unscaled\n  Metric id: nmi\n  Worst score: 0.6876227462352709%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_full_unscaled nmi", 
-        "value": 0.8465520783703908, 
-        "severity": 0, 
-        "severity_value": 0.4232760391851954, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_full_unscaled\n  Metric id: nmi\n  Best score: 0.8465520783703908%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_hvg_scaled nmi", 
-        "value": 0.6076171501047576, 
-        "severity": 0, 
-        "severity_value": -0.6076171501047576, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.6076171501047576%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_hvg_scaled nmi", 
-        "value": 0.86450151417597, 
-        "severity": 0, 
-        "severity_value": 0.432250757087985, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_scaled\n  Metric id: nmi\n  Best score: 0.86450151417597%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score bbknn_hvg_unscaled nmi", 
-        "value": 0.7273703498805031, 
-        "severity": 0, 
-        "severity_value": -0.7273703498805031, 
-        "code": "worst_score >= -1", 
-        "message": "Method bbknn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7273703498805031%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score bbknn_hvg_unscaled nmi", 
-        "value": 0.9150555033121013, 
-        "severity": 0, 
-        "severity_value": 0.45752775165605064, 
-        "code": "best_score <= 2", 
-        "message": "Method bbknn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: bbknn_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.9150555033121013%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_graph nmi", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_graph performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_graph\n  Metric id: nmi\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_graph nmi", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_graph performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_graph\n  Metric id: nmi\n  Best score: 1.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score celltype_random_integration nmi", 
-        "value": 0.5911119959537785, 
-        "severity": 0, 
-        "severity_value": -0.5911119959537785, 
-        "code": "worst_score >= -1", 
-        "message": "Method celltype_random_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_integration\n  Metric id: nmi\n  Worst score: 0.5911119959537785%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score celltype_random_integration nmi", 
-        "value": 0.6905001085993717, 
-        "severity": 0, 
-        "severity_value": 0.34525005429968586, 
-        "code": "best_score <= 2", 
-        "message": "Method celltype_random_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: celltype_random_integration\n  Metric id: nmi\n  Best score: 0.6905001085993717%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_scaled nmi", 
-        "value": 0.4356852966429723, 
-        "severity": 0, 
-        "severity_value": -0.4356852966429723, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_scaled\n  Metric id: nmi\n  Worst score: 0.4356852966429723%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_scaled nmi", 
-        "value": 0.7466286996340442, 
-        "severity": 0, 
-        "severity_value": 0.3733143498170221, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_scaled\n  Metric id: nmi\n  Best score: 0.7466286996340442%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_full_unscaled nmi", 
-        "value": 0.7146559361165453, 
-        "severity": 0, 
-        "severity_value": -0.7146559361165453, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_unscaled\n  Metric id: nmi\n  Worst score: 0.7146559361165453%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_full_unscaled nmi", 
-        "value": 0.919153598955348, 
-        "severity": 0, 
-        "severity_value": 0.459576799477674, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_full_unscaled\n  Metric id: nmi\n  Best score: 0.919153598955348%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_scaled nmi", 
-        "value": 0.708708409917688, 
-        "severity": 0, 
-        "severity_value": -0.708708409917688, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.708708409917688%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_scaled nmi", 
-        "value": 0.9184880424130523, 
-        "severity": 0, 
-        "severity_value": 0.45924402120652613, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_scaled\n  Metric id: nmi\n  Best score: 0.9184880424130523%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score combat_hvg_unscaled nmi", 
-        "value": 0.7080440035180836, 
-        "severity": 0, 
-        "severity_value": -0.7080440035180836, 
-        "code": "worst_score >= -1", 
-        "message": "Method combat_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7080440035180836%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score combat_hvg_unscaled nmi", 
-        "value": 0.9086992064806804, 
-        "severity": 0, 
-        "severity_value": 0.4543496032403402, 
-        "code": "best_score <= 2", 
-        "message": "Method combat_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: combat_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.9086992064806804%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_scaled nmi", 
-        "value": 0.6659650552475218, 
-        "severity": 0, 
-        "severity_value": -0.6659650552475218, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_scaled\n  Metric id: nmi\n  Worst score: 0.6659650552475218%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_scaled nmi", 
-        "value": 0.8334115622739414, 
-        "severity": 0, 
-        "severity_value": 0.4167057811369707, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_scaled\n  Metric id: nmi\n  Best score: 0.8334115622739414%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_full_unscaled nmi", 
-        "value": 0.6664794432455935, 
-        "severity": 0, 
-        "severity_value": -0.6664794432455935, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: nmi\n  Worst score: 0.6664794432455935%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_full_unscaled nmi", 
-        "value": 0.8323358048933254, 
-        "severity": 0, 
-        "severity_value": 0.4161679024466627, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_full_unscaled\n  Metric id: nmi\n  Best score: 0.8323358048933254%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_scaled nmi", 
-        "value": 0.7283683347580043, 
-        "severity": 0, 
-        "severity_value": -0.7283683347580043, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.7283683347580043%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_scaled nmi", 
-        "value": 0.8412524158511939, 
-        "severity": 0, 
-        "severity_value": 0.42062620792559696, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_scaled\n  Metric id: nmi\n  Best score: 0.8412524158511939%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_embed_hvg_unscaled nmi", 
-        "value": 0.7245573990719655, 
-        "severity": 0, 
-        "severity_value": -0.7245573990719655, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7245573990719655%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_embed_hvg_unscaled nmi", 
-        "value": 0.8752953136097249, 
-        "severity": 0, 
-        "severity_value": 0.43764765680486245, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_embed_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.8752953136097249%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_scaled nmi", 
-        "value": 0.6238105539467659, 
-        "severity": 0, 
-        "severity_value": -0.6238105539467659, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_scaled\n  Metric id: nmi\n  Worst score: 0.6238105539467659%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_scaled nmi", 
-        "value": 0.8409723880192757, 
-        "severity": 0, 
-        "severity_value": 0.4204861940096378, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_scaled\n  Metric id: nmi\n  Best score: 0.8409723880192757%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_full_unscaled nmi", 
-        "value": 0.625232532691663, 
-        "severity": 0, 
-        "severity_value": -0.625232532691663, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: nmi\n  Worst score: 0.625232532691663%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_full_unscaled nmi", 
-        "value": 0.8406482724651522, 
-        "severity": 0, 
-        "severity_value": 0.4203241362325761, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_full_unscaled\n  Metric id: nmi\n  Best score: 0.8406482724651522%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_scaled nmi", 
-        "value": 0.7211053738605281, 
-        "severity": 0, 
-        "severity_value": -0.7211053738605281, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.7211053738605281%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_scaled nmi", 
-        "value": 0.8242542798895731, 
-        "severity": 0, 
-        "severity_value": 0.41212713994478656, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_scaled\n  Metric id: nmi\n  Best score: 0.8242542798895731%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score fastmnn_feature_hvg_unscaled nmi", 
-        "value": 0.7199679772887732, 
-        "severity": 0, 
-        "severity_value": -0.7199679772887732, 
-        "code": "worst_score >= -1", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7199679772887732%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score fastmnn_feature_hvg_unscaled nmi", 
-        "value": 0.8409867156411521, 
-        "severity": 0, 
-        "severity_value": 0.42049335782057606, 
-        "code": "best_score <= 2", 
-        "message": "Method fastmnn_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: fastmnn_feature_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.8409867156411521%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_scaled nmi", 
-        "value": 0.5550657845809603, 
-        "severity": 0, 
-        "severity_value": -0.5550657845809603, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_scaled\n  Metric id: nmi\n  Worst score: 0.5550657845809603%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_scaled nmi", 
-        "value": 0.8095529424371669, 
-        "severity": 0, 
-        "severity_value": 0.40477647121858346, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_scaled\n  Metric id: nmi\n  Best score: 0.8095529424371669%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_full_unscaled nmi", 
-        "value": 0.6752149884860108, 
-        "severity": 0, 
-        "severity_value": -0.6752149884860108, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_unscaled\n  Metric id: nmi\n  Worst score: 0.6752149884860108%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_full_unscaled nmi", 
-        "value": 0.8709947964619181, 
-        "severity": 0, 
-        "severity_value": 0.43549739823095907, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_full_unscaled\n  Metric id: nmi\n  Best score: 0.8709947964619181%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_scaled nmi", 
-        "value": 0.6246593973896138, 
-        "severity": 0, 
-        "severity_value": -0.6246593973896138, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.6246593973896138%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_scaled nmi", 
-        "value": 0.8731116932718119, 
-        "severity": 0, 
-        "severity_value": 0.43655584663590596, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_scaled\n  Metric id: nmi\n  Best score: 0.8731116932718119%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score harmony_hvg_unscaled nmi", 
-        "value": 0.6694702434875519, 
-        "severity": 0, 
-        "severity_value": -0.6694702434875519, 
-        "code": "worst_score >= -1", 
-        "message": "Method harmony_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.6694702434875519%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score harmony_hvg_unscaled nmi", 
-        "value": 0.9167900144297996, 
-        "severity": 0, 
-        "severity_value": 0.4583950072148998, 
-        "code": "best_score <= 2", 
-        "message": "Method harmony_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: harmony_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.9167900144297996%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score liger_full_unscaled nmi", 
-        "value": 0.19163183514934606, 
-        "severity": 0, 
-        "severity_value": -0.19163183514934606, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_full_unscaled\n  Metric id: nmi\n  Worst score: 0.19163183514934606%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score liger_full_unscaled nmi", 
-        "value": 0.6595202898967096, 
-        "severity": 0, 
-        "severity_value": 0.3297601449483548, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_full_unscaled\n  Metric id: nmi\n  Best score: 0.6595202898967096%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score liger_hvg_unscaled nmi", 
-        "value": 0.19764866808255022, 
-        "severity": 0, 
-        "severity_value": -0.19764866808255022, 
-        "code": "worst_score >= -1", 
-        "message": "Method liger_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.19764866808255022%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score liger_hvg_unscaled nmi", 
-        "value": 0.742567857245879, 
-        "severity": 0, 
-        "severity_value": 0.3712839286229395, 
-        "code": "best_score <= 2", 
-        "message": "Method liger_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: liger_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.742567857245879%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_scaled nmi", 
-        "value": 0.5410699093576836, 
-        "severity": 0, 
-        "severity_value": -0.5410699093576836, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_scaled\n  Metric id: nmi\n  Worst score: 0.5410699093576836%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_scaled nmi", 
-        "value": 0.7779445599806406, 
-        "severity": 0, 
-        "severity_value": 0.3889722799903203, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_scaled\n  Metric id: nmi\n  Best score: 0.7779445599806406%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_full_unscaled nmi", 
-        "value": 0.7237032910979424, 
-        "severity": 0, 
-        "severity_value": -0.7237032910979424, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_unscaled\n  Metric id: nmi\n  Worst score: 0.7237032910979424%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_full_unscaled nmi", 
-        "value": 0.8690707087364455, 
-        "severity": 0, 
-        "severity_value": 0.43453535436822277, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_full_unscaled\n  Metric id: nmi\n  Best score: 0.8690707087364455%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_scaled nmi", 
-        "value": 0.7276664131443138, 
-        "severity": 0, 
-        "severity_value": -0.7276664131443138, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.7276664131443138%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_scaled nmi", 
-        "value": 0.9146177746120867, 
-        "severity": 0, 
-        "severity_value": 0.45730888730604335, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_scaled\n  Metric id: nmi\n  Best score: 0.9146177746120867%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score mnn_hvg_unscaled nmi", 
-        "value": 0.7399018134019033, 
-        "severity": 0, 
-        "severity_value": -0.7399018134019033, 
-        "code": "worst_score >= -1", 
-        "message": "Method mnn_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7399018134019033%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score mnn_hvg_unscaled nmi", 
-        "value": 0.8391687275327969, 
-        "severity": 0, 
-        "severity_value": 0.41958436376639846, 
-        "code": "best_score <= 2", 
-        "message": "Method mnn_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: mnn_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.8391687275327969%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score no_integration nmi", 
-        "value": 0.591916349707197, 
-        "severity": 0, 
-        "severity_value": -0.591916349707197, 
-        "code": "worst_score >= -1", 
-        "message": "Method no_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: no_integration\n  Metric id: nmi\n  Worst score: 0.591916349707197%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score no_integration nmi", 
-        "value": 0.6938135994940211, 
-        "severity": 0, 
-        "severity_value": 0.34690679974701055, 
-        "code": "best_score <= 2", 
-        "message": "Method no_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: no_integration\n  Metric id: nmi\n  Best score: 0.6938135994940211%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score random_integration nmi", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": -0.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method random_integration performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: random_integration\n  Metric id: nmi\n  Worst score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score random_integration nmi", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "best_score <= 2", 
-        "message": "Method random_integration performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: random_integration\n  Metric id: nmi\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_full nmi", 
-        "value": 0.7335012282016883, 
-        "severity": 0, 
-        "severity_value": -0.7335012282016883, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_full performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_full\n  Metric id: nmi\n  Worst score: 0.7335012282016883%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scalex_full nmi", 
-        "value": 0.8636685805441228, 
-        "severity": 0, 
-        "severity_value": 0.4318342902720614, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_full performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_full\n  Metric id: nmi\n  Best score: 0.8636685805441228%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scalex_hvg nmi", 
-        "value": 0.7667144691840423, 
-        "severity": 0, 
-        "severity_value": -0.7667144691840423, 
-        "code": "worst_score >= -1", 
-        "message": "Method scalex_hvg performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_hvg\n  Metric id: nmi\n  Worst score: 0.7667144691840423%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scalex_hvg nmi", 
-        "value": 0.9065561314515288, 
-        "severity": 0, 
-        "severity_value": 0.4532780657257644, 
-        "code": "best_score <= 2", 
-        "message": "Method scalex_hvg performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scalex_hvg\n  Metric id: nmi\n  Best score: 0.9065561314515288%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_scaled nmi", 
-        "value": 0.7092690544082199, 
-        "severity": 0, 
-        "severity_value": -0.7092690544082199, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_scaled\n  Metric id: nmi\n  Worst score: 0.7092690544082199%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_scaled nmi", 
-        "value": 0.8790500566449821, 
-        "severity": 0, 
-        "severity_value": 0.4395250283224911, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_scaled\n  Metric id: nmi\n  Best score: 0.8790500566449821%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_full_unscaled nmi", 
-        "value": 0.7057413351677663, 
-        "severity": 0, 
-        "severity_value": -0.7057413351677663, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_unscaled\n  Metric id: nmi\n  Worst score: 0.7057413351677663%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_full_unscaled nmi", 
-        "value": 0.7871349031431972, 
-        "severity": 0, 
-        "severity_value": 0.3935674515715986, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_full_unscaled\n  Metric id: nmi\n  Best score: 0.7871349031431972%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_scaled nmi", 
-        "value": 0.7264776372364677, 
-        "severity": 0, 
-        "severity_value": -0.7264776372364677, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.7264776372364677%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_scaled nmi", 
-        "value": 0.925640336000966, 
-        "severity": 0, 
-        "severity_value": 0.462820168000483, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_scaled\n  Metric id: nmi\n  Best score: 0.925640336000966%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_embed_hvg_unscaled nmi", 
-        "value": 0.7344858957666593, 
-        "severity": 0, 
-        "severity_value": -0.7344858957666593, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_embed_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7344858957666593%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_embed_hvg_unscaled nmi", 
-        "value": 0.9319691559361882, 
-        "severity": 0, 
-        "severity_value": 0.4659845779680941, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_embed_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_embed_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.9319691559361882%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_scaled nmi", 
-        "value": 0.6616530632738331, 
-        "severity": 0, 
-        "severity_value": -0.6616530632738331, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_scaled\n  Metric id: nmi\n  Worst score: 0.6616530632738331%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_scaled nmi", 
-        "value": 0.7467970689054021, 
-        "severity": 0, 
-        "severity_value": 0.37339853445270105, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_scaled\n  Metric id: nmi\n  Best score: 0.7467970689054021%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_full_unscaled nmi", 
-        "value": 0.7042147342768444, 
-        "severity": 0, 
-        "severity_value": -0.7042147342768444, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_unscaled\n  Metric id: nmi\n  Worst score: 0.7042147342768444%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_full_unscaled nmi", 
-        "value": 0.76246641859583, 
-        "severity": 0, 
-        "severity_value": 0.381233209297915, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_full_unscaled\n  Metric id: nmi\n  Best score: 0.76246641859583%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_scaled nmi", 
-        "value": 0.6957699251263042, 
-        "severity": 0, 
-        "severity_value": -0.6957699251263042, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_scaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: nmi\n  Worst score: 0.6957699251263042%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_scaled nmi", 
-        "value": 0.9113209695166642, 
-        "severity": 0, 
-        "severity_value": 0.4556604847583321, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_scaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_scaled\n  Metric id: nmi\n  Best score: 0.9113209695166642%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanorama_feature_hvg_unscaled nmi", 
-        "value": 0.7216364086199782, 
-        "severity": 0, 
-        "severity_value": -0.7216364086199782, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanorama_feature_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7216364086199782%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanorama_feature_hvg_unscaled nmi", 
-        "value": 0.8246645668593021, 
-        "severity": 0, 
-        "severity_value": 0.41233228342965106, 
-        "code": "best_score <= 2", 
-        "message": "Method scanorama_feature_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanorama_feature_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.8246645668593021%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_full_unscaled nmi", 
-        "value": 0.8016518289741844, 
-        "severity": 0, 
-        "severity_value": -0.8016518289741844, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_full_unscaled\n  Metric id: nmi\n  Worst score: 0.8016518289741844%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_full_unscaled nmi", 
-        "value": 0.920763347424091, 
-        "severity": 0, 
-        "severity_value": 0.4603816737120455, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_full_unscaled\n  Metric id: nmi\n  Best score: 0.920763347424091%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scanvi_hvg_unscaled nmi", 
-        "value": 0.8285226521053414, 
-        "severity": 0, 
-        "severity_value": -0.8285226521053414, 
-        "code": "worst_score >= -1", 
-        "message": "Method scanvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.8285226521053414%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scanvi_hvg_unscaled nmi", 
-        "value": 0.9243815723465011, 
-        "severity": 0, 
-        "severity_value": 0.46219078617325055, 
-        "code": "best_score <= 2", 
-        "message": "Method scanvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scanvi_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.9243815723465011%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_full_unscaled nmi", 
-        "value": 0.7488243449149337, 
-        "severity": 0, 
-        "severity_value": -0.7488243449149337, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_full_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_full_unscaled\n  Metric id: nmi\n  Worst score: 0.7488243449149337%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scvi_full_unscaled nmi", 
-        "value": 0.9128931077077378, 
-        "severity": 0, 
-        "severity_value": 0.4564465538538689, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_full_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_full_unscaled\n  Metric id: nmi\n  Best score: 0.9128931077077378%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Worst score scvi_hvg_unscaled nmi", 
-        "value": 0.7328466103093703, 
-        "severity": 0, 
-        "severity_value": -0.7328466103093703, 
-        "code": "worst_score >= -1", 
-        "message": "Method scvi_hvg_unscaled performs much worse than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_hvg_unscaled\n  Metric id: nmi\n  Worst score: 0.7328466103093703%\n"
-    }, 
-    {
-        "task_id": "batch_integration_graph", 
-        "category": "Scaling", 
-        "name": "Best score scvi_hvg_unscaled nmi", 
-        "value": 0.9150189227094987, 
-        "severity": 0, 
-        "severity_value": 0.45750946135474935, 
-        "code": "best_score <= 2", 
-        "message": "Method scvi_hvg_unscaled performs a lot better than baselines.\n  Task id: batch_integration_graph\n  Method id: scvi_hvg_unscaled\n  Metric id: nmi\n  Best score: 0.9150189227094987%\n"
-    }
-]
\ No newline at end of file
diff --git a/results/batch_integration_graph/data/results.json b/results/batch_integration_graph/data/results.json
deleted file mode 100644
index 9225012a..00000000
--- a/results/batch_integration_graph/data/results.json
+++ /dev/null
@@ -1,3782 +0,0 @@
-[
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\ No newline at end of file
diff --git a/results/batch_integration_graph/data/task_info.json b/results/batch_integration_graph/data/task_info.json
deleted file mode 100644
index 925ca27d..00000000
--- a/results/batch_integration_graph/data/task_info.json
+++ /dev/null
@@ -1,68 +0,0 @@
-{
-  "task_id": "batch_integration_graph",
-  "commit_sha": "c97decf07adb2e3050561d6fa9ae46132be07bef",
-  "task_name": "Batch integration graph",
-  "task_summary": "Removing batch effects while preserving biological variation (graph output)",
-  "task_description": "\nThis is a sub-task of the overall batch integration task. Batch (or data) integration\nmethods integrate datasets across batches that arise from various biological and\ntechnical sources. Methods that integrate batches typically have three different types\nof output: a corrected feature matrix, a joint embedding across batches, and/or an\nintegrated cell-cell similarity graph (e.g., a kNN graph). This sub-task focuses on all\nmethods that can output integrated graphs, and includes methods that canonically output\nthe other two data formats with subsequent postprocessing to generate a graph. Other\nsub-tasks for batch integration can be found for:\n\n* [embeddings](../batch_integration_embed/), and\n* [corrected features](../batch_integration_feature/)\n\nThis sub-task was taken from a [benchmarking study of data integration\nmethods](https://openproblems.bio/bibliography#luecken2022benchmarking).\n\n",
-  "repo": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks/_batch_integration/batch_integration_graph",
-  "authors": [
-    {
-      "name": "Michaela Mueller",
-      "roles": ["maintainer", "author"],
-      "info": {
-        "github": "mumichae",
-        "orcid": "0000-0002-1401-1785"
-      }
-    },
-    {
-      "name": "Malte Luecken",
-      "roles": "author",
-      "info": {
-        "github": "LuckyMD",
-        "orcid": "0000-0001-7464-7921"
-      }
-    },
-    {
-      "name": "Daniel Strobl",
-      "roles": "author",
-      "info": {
-        "github": "danielStrobl",
-        "orcid": "0000-0002-5516-7057"
-      }
-    },
-    {
-      "name": "Robrecht Cannoodt",
-      "roles": "contributor",
-      "info": {
-        "github": "rcannood",
-        "orcid": "0000-0003-3641-729X"
-      }
-    },
-    {
-      "name": "Scott Gigante",
-      "roles": "contributor",
-      "info": {
-        "github": "scottgigante",
-        "orcid": "0000-0002-4544-2764"
-      }
-    },
-    {
-      "name": "Kai Waldrant",
-      "roles": "contributor",
-      "info": {
-        "github": "KaiWaldrant",
-        "orcid": "0009-0003-8555-1361"
-      }
-    },
-    {
-      "name": "Nartin Kim",
-      "roles": "contributor",
-      "info": {
-        "github": "martinkim0",
-        "orcid": "0009-0003-8555-1361"
-      }
-    }
-  ],
-  "version": "v1.0.0",
-  "license": "MIT"
-}
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deleted file mode 100644
index c36b1f49..00000000
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diff --git a/results/batch_integration_graph/thumbnail.svg b/results/batch_integration_graph/thumbnail.svg
deleted file mode 100644
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