{"id":"W4237264985","doi":"10.1515/iupac.79.1321","title":"Gene Expression","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Multidisciplinary approach; Hazard; Toxicology; Chemistry; Biology; Linguistics; Philosophy; Sociology; Social science","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":[0.0002240508,0.0003402509,0.000278273,0.0001078061,0.00009748779,0.00003319529,0.0004477324,0.0005597091,0.001002561],"category_scores_gemma":[0.0001817802,0.0002511856,0.0001563742,0.00008115014,0.00008437425,0.000003050167,0.0002392059,0.0001838619,0.000002905129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008122138,"about_ca_system_score_gemma":0.0005327135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007195999,"about_ca_topic_score_gemma":0.00003686241,"domain_scores_codex":[0.9980426,0.00008913023,0.0003274083,0.0006753416,0.0005667513,0.0002987531],"domain_scores_gemma":[0.9981139,0.000007492487,0.0002474406,0.001162946,0.0003057627,0.0001624027],"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":[0.0001442142,0.00007422559,0.000003725273,0.00002658749,0.00002404288,0.000003452173,0.000001333013,0.00000101004,0.2181534,4.255527e-7,0.7794119,0.002155716],"study_design_scores_gemma":[0.0005962573,0.0001512564,0.00001681765,0.0001309665,0.0000253831,0.000007507438,0.000007795015,3.120832e-7,0.1273154,0.00001976435,0.871419,0.000309539],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004631603,0.001663905,0.0008668261,0.0002666239,0.000754712,0.0001917913,0.9957166,0.00001968468,0.00005666837],"genre_scores_gemma":[0.0002066818,0.003327138,0.000101527,0.0003600473,0.001793756,0.00004146377,0.992497,0.00004010201,0.00163233],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09200709,"threshold_uncertainty_score":0.999994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01293370134187388,"score_gpt":0.3806280263808769,"score_spread":0.367694325039003,"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."}}