Method

Random Labels

Negative control that predicts cell-type labels at random without training on the data.

Overview

An explanatory profile has not yet been reviewed for this record.

Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.

Evaluations and results

24 evaluations · 24 results. Different protocols are not a single leaderboard.

Sorted by Accuracy (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	Protocol and dataset	Accuracy	Macro F1	Weighted F1
Random LabelsEvaluation	Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), Accuracy, Pancreas (random split) (Open Problems label projection split)	0.221 (best in column)	Not reported	Not reported
Random LabelsEvaluation	Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy, Pancreas (by batch) (Open Problems label projection split)	0.213	Not reported	Not reported
Random LabelsEvaluation	Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy, Pancreas (random split with label noise) (Open Problems label projection split)	0.173	Not reported	Not reported
Random LabelsEvaluation	Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), Accuracy, Tabula Muris Senis Lung (random split) (Open Problems label projection split)	0.0893	Not reported	Not reported
Random LabelsEvaluation	Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy, Zebrafish (random split) (Open Problems label projection split)	0.0874	Not reported	Not reported
Random LabelsEvaluation	Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy, Zebrafish (by laboratory) (Open Problems label projection split)	0.0278	Not reported	Not reported
Random LabelsEvaluation	Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), Accuracy, CeNGEN (random split) (Open Problems label projection split)	0.0166	Not reported	Not reported
Random LabelsEvaluation	Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), Accuracy, CeNGEN (split by batch) (Open Problems label projection split)	0.0144	Not reported	Not reported
Random LabelsEvaluation	Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 score, CeNGEN (split by batch) (Open Problems label projection split)	Not reported	Not reported	0.0236
Random LabelsEvaluation	Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 score, CeNGEN (split by batch) (Open Problems label projection split)	Not reported	0.00218	Not reported
Random LabelsEvaluation	Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 score, CeNGEN (random split) (Open Problems label projection split)	Not reported	Not reported	0.0166
Random LabelsEvaluation	Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 score, CeNGEN (random split) (Open Problems label projection split)	Not reported	0.00526	Not reported
Random LabelsEvaluation	Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 score, Pancreas (by batch) (Open Problems label projection split)	Not reported	Not reported	0.214
Random LabelsEvaluation	Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 score, Pancreas (by batch) (Open Problems label projection split)	Not reported	0.0707	Not reported
Random LabelsEvaluation	Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score, Pancreas (random split) (Open Problems label projection split)	Not reported	Not reported	0.221 (best in column)
Random LabelsEvaluation	Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score, Pancreas (random split) (Open Problems label projection split)	Not reported	0.0762 (best in column)	Not reported
Random LabelsEvaluation	Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 score, Pancreas (random split with label noise) (Open Problems label projection split)	Not reported	Not reported	0.188
Random LabelsEvaluation	Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 score, Pancreas (random split with label noise) (Open Problems label projection split)	Not reported	0.0626	Not reported
Random LabelsEvaluation	Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 score, Tabula Muris Senis Lung (random split) (Open Problems label projection split)	Not reported	Not reported	0.0892
Random LabelsEvaluation	Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 score, Tabula Muris Senis Lung (random split) (Open Problems label projection split)	Not reported	0.0253	Not reported
Random LabelsEvaluation	Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score, Zebrafish (by laboratory) (Open Problems label projection split)	Not reported	Not reported	0.0387
Random LabelsEvaluation	Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score, Zebrafish (by laboratory) (Open Problems label projection split)	Not reported	0.0151	Not reported
Random LabelsEvaluation	Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score, Zebrafish (random split) (Open Problems label projection split)	Not reported	Not reported	0.0877
Random LabelsEvaluation	Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score, Zebrafish (random split) (Open Problems label projection split)	Not reported	0.0412	Not reported

All 24 result rows with coverage, uncertainty and sources

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Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Method: Random Labels	Task: Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), Accuracy Dataset subset: CeNGEN (split by batch) (Open Problems label projection split)	0.0144 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection CENGEN-BATCH-ACCURACY: Label projection on CeNGEN (split by batch), Accuracy 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test by experimental batch. Dimensions: 100955 cells, 22469 genes. 169 cell types (avg. 597±800 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(random_labels), paramset(none), metric(accuracy)
Method: Random Labels	Task: Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 score Dataset subset: CeNGEN (split by batch) (Open Problems label projection split)	0.0236 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection CENGEN-BATCH-F1: Label projection on CeNGEN (split by batch), F1 score 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test by experimental batch. Dimensions: 100955 cells, 22469 genes. 169 cell types (avg. 597±800 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(random_labels), paramset(none), metric(f1)
Method: Random Labels	Task: Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 score Dataset subset: CeNGEN (split by batch) (Open Problems label projection split)	0.00218 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection CENGEN-BATCH-F1-MACRO: Label projection on CeNGEN (split by batch), Macro F1 score 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test by experimental batch. Dimensions: 100955 cells, 22469 genes. 169 cell types (avg. 597±800 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_batch), method(random_labels), paramset(none), metric(f1_macro)
Method: Random Labels	Task: Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), Accuracy Dataset subset: CeNGEN (random split) (Open Problems label projection split)	0.0166 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection CENGEN-RANDOM-ACCURACY: Label projection on CeNGEN (random split), Accuracy 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test randomly. Dimensions: 100955 cells, 22469 genes. 169 cell types avg. 597±800 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_random), method(random_labels), paramset(none), metric(accuracy)
Method: Random Labels	Task: Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 score Dataset subset: CeNGEN (random split) (Open Problems label projection split)	0.0166 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection CENGEN-RANDOM-F1: Label projection on CeNGEN (random split), F1 score 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test randomly. Dimensions: 100955 cells, 22469 genes. 169 cell types avg. 597±800 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_random), method(random_labels), paramset(none), metric(f1)
Method: Random Labels	Task: Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 score Dataset subset: CeNGEN (random split) (Open Problems label projection split)	0.00526 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection CENGEN-RANDOM-F1-MACRO: Label projection on CeNGEN (random split), Macro F1 score 100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics. Split into train/test randomly. Dimensions: 100955 cells, 22469 genes. 169 cell types avg. 597±800 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(cengen_random), method(random_labels), paramset(none), metric(f1_macro)
Method: Random Labels	Task: Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Dataset subset: Pancreas (by batch) (Open Problems label projection split)	0.213 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection PANCREAS-BATCH-ACCURACY: Label projection on Pancreas (by batch), Accuracy Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(random_labels), paramset(none), metric(accuracy)
Method: Random Labels	Task: Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 score Dataset subset: Pancreas (by batch) (Open Problems label projection split)	0.214 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection PANCREAS-BATCH-F1: Label projection on Pancreas (by batch), F1 score Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(random_labels), paramset(none), metric(f1)
Method: Random Labels	Task: Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 score Dataset subset: Pancreas (by batch) (Open Problems label projection split)	0.0707 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection PANCREAS-BATCH-F1-MACRO: Label projection on Pancreas (by batch), Macro F1 score Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test by experimental batch. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_batch), method(random_labels), paramset(none), metric(f1_macro)
Method: Random Labels	Task: Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), Accuracy Dataset subset: Pancreas (random split) (Open Problems label projection split)	0.221 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection PANCREAS-RANDOM-ACCURACY: Label projection on Pancreas (random split), Accuracy Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(random_labels), paramset(none), metric(accuracy)
Method: Random Labels	Task: Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score Dataset subset: Pancreas (random split) (Open Problems label projection split)	0.221 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection PANCREAS-RANDOM-F1: Label projection on Pancreas (random split), F1 score Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(random_labels), paramset(none), metric(f1)
Method: Random Labels	Task: Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score Dataset subset: Pancreas (random split) (Open Problems label projection split)	0.0762 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection PANCREAS-RANDOM-F1-MACRO: Label projection on Pancreas (random split), Macro F1 score Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random), method(random_labels), paramset(none), metric(f1_macro)
Method: Random Labels	Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy Dataset subset: Pancreas (random split with label noise) (Open Problems label projection split)	0.173 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-ACCURACY: Label projection on Pancreas (random split with label noise), Accuracy Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly with 20% label noise. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(random_labels), paramset(none), metric(accuracy)
Method: Random Labels	Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 score Dataset subset: Pancreas (random split with label noise) (Open Problems label projection split)	0.188 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1: Label projection on Pancreas (random split with label noise), F1 score Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly with 20% label noise. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(random_labels), paramset(none), metric(f1)
Method: Random Labels	Task: Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 score Dataset subset: Pancreas (random split with label noise) (Open Problems label projection split)	0.0626 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection PANCREAS-RANDOM-LABEL-NOISE-F1-MACRO: Label projection on Pancreas (random split with label noise), Macro F1 score Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Split into train/test randomly with 20% label noise. Dimensions: 16382 cells, 18771 genes. 14 cell types (avg. 1170±1703 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(pancreas_random_label_noise), method(random_labels), paramset(none), metric(f1_macro)
Method: Random Labels	Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), Accuracy Dataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split)	0.0893 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-ACCURACY: Label projection on Tabula Muris Senis Lung (random split), Accuracy All lung cells from Tabula Muris Senis, a 500k cell-atlas from 18 organs and tissues across the mouse lifespan. Split into train/test randomly. Dimensions: 24540 cells, 17985 genes. 39 cell types (avg. 629±999 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(random_labels), paramset(none), metric(accuracy)
Method: Random Labels	Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 score Dataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split)	0.0892 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1: Label projection on Tabula Muris Senis Lung (random split), F1 score All lung cells from Tabula Muris Senis, a 500k cell-atlas from 18 organs and tissues across the mouse lifespan. Split into train/test randomly. Dimensions: 24540 cells, 17985 genes. 39 cell types (avg. 629±999 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(random_labels), paramset(none), metric(f1)
Method: Random Labels	Task: Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 score Dataset subset: Tabula Muris Senis Lung (random split) (Open Problems label projection split)	0.0253 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection TABULA-MURIS-SENIS-LUNG-RANDOM-F1-MACRO: Label projection on Tabula Muris Senis Lung (random split), Macro F1 score All lung cells from Tabula Muris Senis, a 500k cell-atlas from 18 organs and tissues across the mouse lifespan. Split into train/test randomly. Dimensions: 24540 cells, 17985 genes. 39 cell types (avg. 629±999 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(tabula_muris_senis_lung_random), method(random_labels), paramset(none), metric(f1_macro)
Method: Random Labels	Task: Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy Dataset subset: Zebrafish (by laboratory) (Open Problems label projection split)	0.0278 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection ZEBRAFISH-LABS-ACCURACY: Label projection on Zebrafish (by laboratory), Accuracy 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test by laboratory. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(random_labels), paramset(none), metric(accuracy)
Method: Random Labels	Task: Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score Dataset subset: Zebrafish (by laboratory) (Open Problems label projection split)	0.0387 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection ZEBRAFISH-LABS-F1: Label projection on Zebrafish (by laboratory), F1 score 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test by laboratory. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(random_labels), paramset(none), metric(f1)
Method: Random Labels	Task: Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score Dataset subset: Zebrafish (by laboratory) (Open Problems label projection split)	0.0151 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection ZEBRAFISH-LABS-F1-MACRO: Label projection on Zebrafish (by laboratory), Macro F1 score 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test by laboratory. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_labs), method(random_labels), paramset(none), metric(f1_macro)
Method: Random Labels	Task: Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy Dataset subset: Zebrafish (random split) (Open Problems label projection split)	0.0874 Accuracy fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection ZEBRAFISH-RANDOM-ACCURACY: Label projection on Zebrafish (random split), Accuracy 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test randomly. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(random_labels), paramset(none), metric(accuracy)
Method: Random Labels	Task: Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score Dataset subset: Zebrafish (random split) (Open Problems label projection split)	0.0877 Weighted F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection ZEBRAFISH-RANDOM-F1: Label projection on Zebrafish (random split), F1 score 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test randomly. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(random_labels), paramset(none), metric(f1)
Method: Random Labels	Task: Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score Dataset subset: Zebrafish (random split) (Open Problems label projection split)	0.0412 Macro F1 fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Random Labels on Open Problems label projection ZEBRAFISH-RANDOM-F1-MACRO: Label projection on Zebrafish (random split), Macro F1 score 90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene. Split into train/test randomly. Dimensions: 26022 cells, 25258 genes. 24 cell types (avg. 1084±1156 cells per cell type). Aggregation: Not reported openproblems-label primary benchmark evidence · results, dataset(zebrafish_random), method(random_labels), paramset(none), metric(f1_macro)

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Stable ID: open-problems-method-random-labels

areas: cells-tissues
source locator: results, method(random_labels), paramset(none)
missing metadata: checkpoint revision: reason: unreported; parameters: reason: unextracted
provenance note: Control method included by Open Problems in the label projection task to bound the score scale.

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