{"id":"W4234018493","doi":"10.1515/iupac.79.0766","title":"Added Risk","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","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; Linguistics; Political science; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003248333,0.001639266,0.001901989,0.005247464,0.001034021,0.005212488,0.002967344,0.002352434,0.3199126],"category_scores_gemma":[0.04296112,0.0007504767,0.003747068,0.007775308,0.0004745163,0.003724751,0.003484143,0.002802202,0.2116994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001923698,"about_ca_system_score_gemma":0.004332492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0122716,"about_ca_topic_score_gemma":0.02159526,"domain_scores_codex":[0.9943217,0.001206801,0.001098855,0.001437785,0.001533127,0.0004016412],"domain_scores_gemma":[0.9835834,0.005661793,0.001873272,0.003602974,0.004562544,0.0007160509],"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":[0.0001178909,0.00001525549,0.001579321,0.001932051,0.0001276183,0.00002170055,0.00002377321,0.0002682261,0.000043788,0.001409325,0.9788768,0.01558426],"study_design_scores_gemma":[0.000167638,0.00001627316,0.002197587,0.001382593,0.0001149481,0.00007432522,0.00005007717,0.0002017677,0.00008400869,0.003645998,0.992038,0.00002673275],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001321934,0.0004172509,0.0002772514,0.0004308914,0.0001676101,0.0000630801,0.9931943,0.0003614256,0.004955853],"genre_scores_gemma":[0.001524899,0.0006130169,0.001260485,0.0009783575,0.0001452545,0.0003376708,0.9887651,0.0002650608,0.006110066],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6800874,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01383052724980515,"score_gpt":0.4135226014321362,"score_spread":0.399692074182331,"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."}}