{"id":"W2995421993","doi":"10.1101/2019.12.20.884916","title":"Cell type prioritization in single-cell data","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Collaboration On Repair Discoveries; University of Calgary; Libin Cardiovascular Institute of Alberta; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"National Institute of Neurological Disorders and Stroke; Alberta Innovates; Killam Trusts; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Genome British Columbia; Western Canada Research Grid; Fondation Brain Canada; National Science Foundation; Compute Canada; Canadian Institutes of Health Research; Genome Canada","keywords":"Compendium; Computer science; Prioritization; Cell; Cell type; Chromatin; Neuroscience; Artificial intelligence; Computational biology; Biology; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002638666,0.001234302,0.001491877,0.002578399,0.0008088097,0.001997644,0.001380117,0.001308127,0.004503215],"category_scores_gemma":[0.007709701,0.0004970258,0.001257984,0.002288665,0.0005743292,0.0009756613,0.001217913,0.002095995,0.003613815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005268829,"about_ca_system_score_gemma":0.001553528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003066095,"about_ca_topic_score_gemma":0.008277479,"domain_scores_codex":[0.9985102,0.0001777861,0.000119571,0.0006937293,0.0003441328,0.0001544848],"domain_scores_gemma":[0.9967513,0.00156944,0.0002549168,0.0007659828,0.0005149244,0.0001433937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00150879,0.0003400741,0.05457028,0.002079144,0.000629863,0.0008380255,0.000607284,0.03288561,0.4292871,0.007563883,0.08391034,0.3857796],"study_design_scores_gemma":[0.0003145874,0.0002913678,0.07635141,0.0002296142,0.0005413389,0.001674621,0.0004573297,0.4363935,0.2532573,0.04903421,0.1811839,0.000270792],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08897857,0.00212635,0.8142252,0.0009161509,0.0007554833,0.0004428509,0.05913255,0.02942987,0.003992962],"genre_scores_gemma":[0.2337055,0.0007166076,0.6580381,0.001051224,0.000328361,0.001193823,0.09828197,0.003262278,0.003422152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004503215,"threshold_uncertainty_score":0.01506478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02464313619383108,"score_gpt":0.2220554974913286,"score_spread":0.1974123612974975,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}