{"id":"W4229739013","doi":"10.1515/iupac.79.1963","title":"Risk Characterization","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Chemical Safety and Risk Management","field":"Chemical Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Toxicology; Hazard; Chemistry; Philosophy; Biology; Linguistics; 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":[0.002227077,0.001907695,0.001499047,0.005418578,0.0005640045,0.00269517,0.00227327,0.001851937,0.07272875],"category_scores_gemma":[0.01916062,0.0004899428,0.003502273,0.005332469,0.0003279577,0.002055067,0.001572707,0.002027409,0.04348843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002082427,"about_ca_system_score_gemma":0.00297774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01539928,"about_ca_topic_score_gemma":0.02381438,"domain_scores_codex":[0.9972825,0.0004463482,0.0006122066,0.0008464571,0.0006208982,0.0001915006],"domain_scores_gemma":[0.9933659,0.002936266,0.0008579439,0.001161127,0.001465596,0.0002131936],"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":[0.0003419017,0.0001024869,0.01170973,0.003936943,0.0003448351,0.00009108683,0.00004862808,0.004456518,0.0002053282,0.003143458,0.9420286,0.03359057],"study_design_scores_gemma":[0.0003702296,0.0000662639,0.01126139,0.001407144,0.0002222343,0.0002607093,0.0001237191,0.003425859,0.0004398476,0.007989778,0.9743602,0.00007262143],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004486077,0.0003547797,0.000465752,0.0001195838,0.00003310191,0.00006881039,0.9961339,0.0002871708,0.002088377],"genre_scores_gemma":[0.002221341,0.0003636224,0.001820232,0.0001631432,0.00002530874,0.0002825103,0.9935166,0.00008252469,0.001524674],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07272875,"threshold_uncertainty_score":0.2433019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006987474755531519,"score_gpt":0.3245966713371345,"score_spread":0.317609196581603,"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."}}