{"id":"W4250847417","doi":"10.1515/iupac.79.1950","title":"Returned Effect of Poisons","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; Toxicology; Forensic toxicology; Hazard; Computer science; Chemistry; Biology; Philosophy; 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":[0.001169926,0.001792981,0.001572804,0.003510666,0.0005811122,0.00243244,0.001846832,0.001641997,0.06539076],"category_scores_gemma":[0.01246052,0.0004690232,0.002769669,0.003944831,0.0003190122,0.001524027,0.001509352,0.001570574,0.0524885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00155242,"about_ca_system_score_gemma":0.002310981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01872226,"about_ca_topic_score_gemma":0.02819667,"domain_scores_codex":[0.9980991,0.0002630932,0.0004318059,0.0006706514,0.0004019869,0.0001333156],"domain_scores_gemma":[0.9955003,0.001494311,0.0008655763,0.0009273331,0.0009831532,0.0002292548],"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":[0.0007360142,0.00006488978,0.01688009,0.006334071,0.000446738,0.00008310562,0.00004123214,0.00096015,0.0002740176,0.001268124,0.9470699,0.02584173],"study_design_scores_gemma":[0.0005253968,0.0001018669,0.0332709,0.001856904,0.0004629736,0.0004040986,0.00008098922,0.0009076802,0.0006924173,0.002609014,0.9590142,0.00007367538],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004182635,0.0006546107,0.0001065763,0.00009645679,0.00006606946,0.00002397919,0.9971114,0.0001971372,0.00132539],"genre_scores_gemma":[0.002324904,0.0005355053,0.0004577998,0.0001867472,0.00003309539,0.00008999331,0.9947465,0.00004708942,0.001578209],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06539076,"threshold_uncertainty_score":0.2187539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008351658186889375,"score_gpt":0.3854497736754555,"score_spread":0.3770981154885661,"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."}}