{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003993874,0.0004974639,0.0004298284,0.0001594714,0.00005855464,0.0001488346,0.001143866,0.0008864579,0.00001884031],"category_scores_gemma":[0.0001132749,0.0005856781,0.00008825167,0.0002934585,0.00006839012,0.00001668359,0.0008946839,0.0005454415,0.00006971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009856701,"about_ca_system_score_gemma":0.0006640331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000660599,"about_ca_topic_score_gemma":0.00001332905,"domain_scores_codex":[0.9972281,0.0001243877,0.0005098183,0.001387948,0.0002550885,0.0004946984],"domain_scores_gemma":[0.9967543,0.0000187172,0.0002740377,0.002512416,0.000293524,0.0001469443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006735553,0.000367771,0.01623801,0.0004005348,0.00002745909,0.00001234949,0.000004167515,0.0003124324,0.9814338,0.000007895284,0.001126419,0.000001802853],"study_design_scores_gemma":[0.001095399,0.0001905383,0.009357425,0.0001887375,0.00007179574,1.379414e-8,0.000002545615,0.0009744019,0.9626614,7.269083e-7,0.02451841,0.0009386096],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898604,0.003899665,0.002777562,0.00004860065,0.002153748,0.0006952975,0.00035898,0.00007990697,0.000125795],"genre_scores_gemma":[0.9947899,0.0008648408,0.003288709,0.0002044694,0.0005836167,0.00001892743,0.00003612436,0.000159021,0.00005441152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02339199,"threshold_uncertainty_score":0.9996595,"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."}}