{"id":"W4229558117","doi":"10.1515/iupac.79.1920","title":"Regulatory Dose","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; Computer science; Hazard; Multidisciplinary approach; Toxicology; Chemistry; Philosophy; Biology; Political science; Linguistics; Law","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":[0.002424394,0.001273804,0.001569863,0.003634681,0.0006973386,0.003145782,0.002608908,0.001725445,0.1895622],"category_scores_gemma":[0.02312345,0.0005159198,0.002205445,0.00510824,0.0003684647,0.002255185,0.001515051,0.002198549,0.1286597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001834504,"about_ca_system_score_gemma":0.00295721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01710941,"about_ca_topic_score_gemma":0.02010709,"domain_scores_codex":[0.9969619,0.0005447521,0.0006444703,0.0008907726,0.0007642495,0.0001937361],"domain_scores_gemma":[0.9913722,0.003020324,0.001090183,0.001581466,0.002641812,0.0002939566],"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":[0.0002781882,0.0000373635,0.003281853,0.001644268,0.0001086809,0.00003162537,0.00003134103,0.0004371178,0.0000749157,0.001970938,0.96659,0.02551366],"study_design_scores_gemma":[0.0002058096,0.00002814515,0.005055669,0.0008006518,0.00008180366,0.00008683971,0.00005574457,0.0002034798,0.0001547064,0.002157487,0.9911424,0.0000272449],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000346861,0.0005008391,0.0003424143,0.0002648022,0.0001526377,0.0000787778,0.9876815,0.0003287844,0.01030343],"genre_scores_gemma":[0.003223969,0.0008237779,0.001256622,0.0008676265,0.0001062394,0.0003910654,0.9831845,0.0002257537,0.009920416],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1895622,"threshold_uncertainty_score":0.6341486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01267337687264173,"score_gpt":0.3787022682830863,"score_spread":0.3660288914104445,"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."}}