{"id":"W4247979293","doi":"10.1515/iupac.79.2028","title":"Soporific","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Multidisciplinary approach; Computer science; Toxicology; Library science; Chemistry; Philosophy; Biology; Sociology; Linguistics; Social science; Organic chemistry","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":[0.001287802,0.001909143,0.001618184,0.003763216,0.0009742234,0.003306538,0.002678068,0.001678592,0.1247565],"category_scores_gemma":[0.007437903,0.0006340493,0.001776195,0.005780981,0.0004364398,0.0020519,0.002498436,0.001793027,0.1994926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001354297,"about_ca_system_score_gemma":0.003214641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01396049,"about_ca_topic_score_gemma":0.02957604,"domain_scores_codex":[0.9985219,0.0002494406,0.0001990754,0.0005211336,0.0003220929,0.0001864481],"domain_scores_gemma":[0.9976828,0.000504714,0.000282499,0.0006641522,0.0005837537,0.0002821716],"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":[0.0001142605,0.00001872784,0.0008976571,0.0009122188,0.00004035889,0.00001679719,0.00001700133,0.0001923178,0.0001006437,0.0006484755,0.9923636,0.004677942],"study_design_scores_gemma":[0.0001840716,0.00001766983,0.002364009,0.0003588425,0.0000343562,0.000049038,0.00004446684,0.0002148506,0.0002036346,0.00111729,0.9953924,0.00001929376],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009596829,0.0001205242,0.00006456877,0.00006916108,0.00003364418,0.00001504348,0.9980039,0.0003289126,0.001268301],"genre_scores_gemma":[0.0002269571,0.00009704079,0.0002264533,0.00008017376,0.000009012825,0.00006360292,0.9983566,0.00006892381,0.0008711187],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1247565,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348496614634432,"score_gpt":0.3885164969011323,"score_spread":0.375031530754788,"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."}}