{"id":"W4253233389","doi":"10.1515/iupac.79.1158","title":"Dominant","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Toxicology; Library science; Chemistry; Philosophy; Biology; Linguistics","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":[],"consensus_categories":[],"category_scores_codex":[0.001524344,0.002167795,0.001601737,0.004852545,0.001387264,0.004592987,0.002924467,0.001753977,0.1864753],"category_scores_gemma":[0.01487066,0.0006913036,0.002010737,0.008074084,0.0004353804,0.003200598,0.002582259,0.00191381,0.268779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001945868,"about_ca_system_score_gemma":0.003623766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02505949,"about_ca_topic_score_gemma":0.04708978,"domain_scores_codex":[0.9970066,0.0004772402,0.0004768226,0.001050294,0.0006375162,0.0003514678],"domain_scores_gemma":[0.9944662,0.001350434,0.0005357494,0.001439502,0.001881207,0.0003268157],"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":[0.0000821236,0.00001344403,0.001095988,0.0007863546,0.00003272569,0.00001509493,0.00003068515,0.0001154534,0.00007930838,0.0008852802,0.9907321,0.006131435],"study_design_scores_gemma":[0.00009791925,0.00001169437,0.002328458,0.0005021595,0.00003220853,0.00005142164,0.0001089668,0.0001614805,0.0001467655,0.001551871,0.9949856,0.00002148112],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001160928,0.000123553,0.0001191107,0.00009074425,0.00004362893,0.00002027336,0.9969912,0.0002737972,0.002221669],"genre_scores_gemma":[0.0004091667,0.0001239392,0.0003753839,0.0001364264,0.00001492599,0.0001111932,0.9967546,0.0001150351,0.00195935],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1864753,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01513916351260595,"score_gpt":0.41872752560904,"score_spread":0.4035883620964341,"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."}}