{"id":"W4394251295","doi":"10.6084/m9.figshare.19625411","title":"Additional file 2 of One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data","year":2022,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; Canadian Institute for Advanced Research; University Health Network","funders":"","keywords":"RNA-Seq; Computer science; Computational biology; Database; Biology; Transcriptome; Genetics; Gene expression; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019143,0.002367075,0.001976569,0.003095537,0.001135613,0.002916583,0.003204609,0.002396374,0.3765263],"category_scores_gemma":[0.00867246,0.0009504302,0.001526087,0.004741807,0.0006549291,0.00174396,0.002188887,0.00205696,0.1451797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001503324,"about_ca_system_score_gemma":0.00311301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01108014,"about_ca_topic_score_gemma":0.02315321,"domain_scores_codex":[0.9989667,0.0001312706,0.0001288176,0.0003870434,0.0002104029,0.000175734],"domain_scores_gemma":[0.9954455,0.002587978,0.0003351039,0.0006842803,0.0006066606,0.000340584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001525348,0.00004404047,0.001600307,0.002781618,0.00009507327,0.00006717176,0.00004867129,0.0008100001,0.0007915368,0.001215905,0.9889085,0.003484661],"study_design_scores_gemma":[0.0008709917,0.00005675082,0.006559337,0.0007132551,0.0001390088,0.0002034596,0.00008618415,0.0009832225,0.001839048,0.005589996,0.982865,0.00009378722],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004403378,0.00002049754,0.0001274411,0.00001745047,0.000009667746,0.00001154754,0.999153,0.0003493026,0.0002670775],"genre_scores_gemma":[0.0006242351,0.00004715046,0.000854118,0.0001088202,0.000008370979,0.0001808995,0.9970946,0.0003600825,0.0007217316],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3765263,"threshold_uncertainty_score":0.8893096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03931225616903376,"score_gpt":0.2615162336240958,"score_spread":0.222203977455062,"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."}}