{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002537356,0.0004048434,0.0004784062,0.00009918882,0.00008100436,0.00003173741,0.000414458,0.0004199251,0.004995774],"category_scores_gemma":[0.0004632138,0.000296739,0.0001892157,0.0001589928,0.00005820752,0.00008191126,0.000249021,0.000592154,0.00001480837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004237957,"about_ca_system_score_gemma":0.00007224485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006430154,"about_ca_topic_score_gemma":0.00003975661,"domain_scores_codex":[0.9978716,0.00003262706,0.0004726452,0.0004754341,0.0007458662,0.0004018686],"domain_scores_gemma":[0.998637,0.0000798552,0.0002818613,0.000684435,0.0001533144,0.0001635661],"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":[0.00008613785,0.0001145459,0.000002705773,0.0001715955,0.000126227,0.00001533191,0.000003696841,0.000008622366,0.001619894,0.00001550581,0.9879,0.009935714],"study_design_scores_gemma":[0.0006164175,0.00003144472,0.00002899548,0.0002986021,0.0001629235,0.000001770722,0.000002029273,0.0001476437,0.0005815208,0.00006276352,0.9976292,0.0004366883],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001254261,0.0001241847,0.006319704,0.0003401392,0.000563633,0.0002074787,0.9921015,0.0001697332,0.00004820247],"genre_scores_gemma":[0.00002252287,0.003952951,0.00003000476,0.0001274398,0.001440554,0.00001562522,0.9934117,0.00004684089,0.000952366],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009729178,"threshold_uncertainty_score":0.9999485,"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."}}