{"id":"W4234757739","doi":"10.1515/iupac.79.1842","title":"Precordial","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; Linguistics; Philosophy; Biology; 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":[0.002486922,0.001816366,0.001854594,0.005866528,0.001375347,0.005483143,0.002699933,0.001974956,0.2552936],"category_scores_gemma":[0.02621878,0.0008086849,0.001880117,0.009738619,0.0005753107,0.003281261,0.002977726,0.002365014,0.2978972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001990798,"about_ca_system_score_gemma":0.004504427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01390357,"about_ca_topic_score_gemma":0.02679023,"domain_scores_codex":[0.99582,0.0007560396,0.000775312,0.001251343,0.0009269535,0.000470399],"domain_scores_gemma":[0.9879232,0.003522397,0.001071968,0.00307014,0.003661379,0.0007509714],"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":[0.00005801771,0.00000837853,0.0007994793,0.0007191976,0.00002190876,0.0000134718,0.0000163287,0.00007544533,0.00004237818,0.0005549549,0.9927484,0.004941983],"study_design_scores_gemma":[0.00008831244,0.000009964651,0.001904773,0.000501241,0.00002454402,0.00004349933,0.00005291387,0.00009294895,0.0001029154,0.001146351,0.9960155,0.00001702305],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008797355,0.0001795424,0.000118295,0.0001739864,0.0001042196,0.00002619363,0.9966744,0.0002361995,0.002399169],"genre_scores_gemma":[0.000468774,0.0002129605,0.000405855,0.0002820191,0.00005351735,0.0001674263,0.9952818,0.0001769566,0.002950762],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2552936,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01550721463179449,"score_gpt":0.4189914630383147,"score_spread":0.4034842484065203,"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."}}