{"id":"W4398362790","doi":"10.7910/dvn/bxiy5w/wjpfdk","title":"GRAY_2017.RData","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Vector Institute; Princess Margaret Cancer Centre","funders":"","keywords":"Gray (unit); Computer science; Medicine; Nuclear medicine","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":["insufficient_payload"],"category_scores_codex":[0.0002212651,0.0003272264,0.0003264332,0.00007251751,0.00006609609,0.00005323317,0.001167831,0.000765686,0.004686217],"category_scores_gemma":[0.0004412428,0.000287894,0.0001356896,0.00006208631,0.0002056158,0.000003291605,0.0009952672,0.0003168533,0.08786348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001394979,"about_ca_system_score_gemma":0.0001648693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009150363,"about_ca_topic_score_gemma":0.00006787248,"domain_scores_codex":[0.9983606,0.00006684141,0.0002464378,0.0007020344,0.0002500384,0.0003740408],"domain_scores_gemma":[0.9972645,0.00002226667,0.0001586022,0.002371886,0.00004718249,0.0001355393],"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":[0.00005054036,0.00005041674,0.000008823735,0.00008289749,0.00009920637,0.00002540783,0.000001617743,6.43554e-7,0.0006768514,0.000001367851,0.9978832,0.001119004],"study_design_scores_gemma":[0.0004010539,0.0001856198,0.00002376586,0.00004486167,0.00007944678,0.0000261603,0.00001948458,0.000002458928,0.0003153986,0.000004794541,0.9985233,0.0003736668],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007220574,0.00001941718,0.00008218453,0.000009841158,0.001173761,0.0001574024,0.9982549,0.00001915711,0.0002111486],"genre_scores_gemma":[0.00001600209,0.001106882,0.0004862164,0.0006429038,0.0006175279,0.00001471373,0.9949129,0.0000235672,0.002179305],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08317727,"threshold_uncertainty_score":0.9999573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02427368322174595,"score_gpt":0.2766527202934732,"score_spread":0.2523790370717273,"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."}}