{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001657368,0.000336298,0.0004626955,0.00009253113,0.000129002,0.0001048444,0.001684142,0.0004029791,0.8641674],"category_scores_gemma":[0.0002658056,0.0003557244,0.00008299533,0.0001849903,0.0001099397,0.000008713976,0.002359726,0.0003570322,0.0003236379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003946839,"about_ca_system_score_gemma":0.0003022705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001865481,"about_ca_topic_score_gemma":0.0004667532,"domain_scores_codex":[0.9980043,0.0001104417,0.0004005156,0.0009637957,0.0002445907,0.000276347],"domain_scores_gemma":[0.9978229,0.0002318495,0.0002849674,0.001407011,0.00007496754,0.0001782772],"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":[0.0002816149,0.0004382185,3.027423e-7,0.00004242626,0.00008707826,0.00001211168,0.00001706396,0.000004524786,0.04320094,6.481762e-8,0.9545433,0.001372378],"study_design_scores_gemma":[0.0003217689,0.0005124194,0.000001959626,0.0001332172,0.0001148424,0.000006795746,0.00002989531,0.00001738885,0.01477176,0.000005680244,0.9836823,0.0004019108],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007887402,0.0001310314,0.00001662628,0.00003690463,0.00008442844,0.0004014464,0.9968015,9.453263e-7,0.001738364],"genre_scores_gemma":[0.00002453676,0.00005897835,0.009234515,0.0001923566,0.000148707,0.0000786616,0.9805302,0.00003476309,0.009697288],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8638437,"threshold_uncertainty_score":0.9998895,"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."}}