{"id":"W4235813761","doi":"10.1515/iupac.79.1966","title":"Risk Estimation","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Risk and Safety Analysis","field":"Decision Sciences","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; Biology; Philosophy; Linguistics; Sociology; Social science","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":[0.003187975,0.002367404,0.001603357,0.004621573,0.0005072347,0.002976746,0.0029515,0.001972794,0.09359414],"category_scores_gemma":[0.02343569,0.0005880285,0.00436956,0.004410157,0.0003126128,0.002104475,0.001325703,0.002243059,0.0656607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001977969,"about_ca_system_score_gemma":0.002725807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01205348,"about_ca_topic_score_gemma":0.01998599,"domain_scores_codex":[0.9971611,0.0006480024,0.0005040219,0.0009107607,0.0006055876,0.0001705874],"domain_scores_gemma":[0.9931565,0.003728836,0.0005692146,0.001159705,0.001215365,0.0001704608],"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":[0.0003502218,0.0001126263,0.007772911,0.002576612,0.0004702548,0.00006738657,0.00002996287,0.009601729,0.0001140224,0.003296802,0.925979,0.04962854],"study_design_scores_gemma":[0.0007165957,0.0001131678,0.006849669,0.001570254,0.0003649197,0.0003111353,0.0001184212,0.01477779,0.0005782493,0.01939431,0.9551121,0.00009348492],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005973839,0.0005542014,0.001593118,0.0002245656,0.00007435719,0.00009380707,0.9932951,0.0008308821,0.002736599],"genre_scores_gemma":[0.003654155,0.000516512,0.005362832,0.0002405204,0.00004680856,0.0003617867,0.9870685,0.0001621288,0.002586782],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09359414,"threshold_uncertainty_score":0.3131036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03730889568320378,"score_gpt":0.4849770834581857,"score_spread":0.447668187774982,"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."}}