{"id":"W4244125689","doi":"10.1515/iupac.81.0786","title":"Risk Source","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; Ecotoxicology; Environmental risk assessment; Relation (database); Computer science; Ecology; Risk assessment; Biology; Data mining; Linguistics; Philosophy","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":[0.001246402,0.001955836,0.001238998,0.006119837,0.0007398376,0.002826073,0.002581238,0.002230233,0.1490024],"category_scores_gemma":[0.01354519,0.0005348081,0.001504904,0.007971525,0.0003455348,0.002349238,0.002261721,0.002605363,0.1538227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002445916,"about_ca_system_score_gemma":0.002616906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02290823,"about_ca_topic_score_gemma":0.03289444,"domain_scores_codex":[0.9981565,0.0002747854,0.0002759964,0.0005232533,0.000576461,0.0001928665],"domain_scores_gemma":[0.9944655,0.001968631,0.0006128704,0.0009322917,0.00173113,0.0002894535],"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":[0.00004591599,0.00001784372,0.001497429,0.0009404146,0.00003189791,0.00002621934,0.00001920463,0.0006860921,0.00005295665,0.001210981,0.9897079,0.005763134],"study_design_scores_gemma":[0.00006690522,0.000007916975,0.002284999,0.0005519229,0.00001878379,0.00005739957,0.00006241696,0.0003987524,0.0001212877,0.002048918,0.9943594,0.00002115264],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000078942,0.00009775956,0.00009608235,0.00009651804,0.00002628204,0.00001211545,0.9977674,0.0001594449,0.001665376],"genre_scores_gemma":[0.0005458829,0.0001409898,0.0004990739,0.0001013917,0.00001203594,0.00007402467,0.997206,0.00006129024,0.001359365],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1490024,"threshold_uncertainty_score":0.4984629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009309508648864907,"score_gpt":0.3657545501893694,"score_spread":0.3564450415405044,"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."}}