{"id":"W4237053881","doi":"10.1515/iupac.76.0317","title":"Negligible Risk","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Historical and Scientific Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; Hazard; Relation (database); Risk assessment; Computer science; Toxicology; Medicine; Chemistry; Pharmacology; Data mining; Biology; Computer security; 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":[0.001284281,0.001349964,0.001144721,0.003526296,0.0005991519,0.002890707,0.001881204,0.001299734,0.1303545],"category_scores_gemma":[0.0135375,0.0005443375,0.00196669,0.004370371,0.0003641605,0.002377594,0.001453506,0.001827197,0.08420245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00116476,"about_ca_system_score_gemma":0.001815074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008508466,"about_ca_topic_score_gemma":0.01479024,"domain_scores_codex":[0.998081,0.0003548642,0.0003991014,0.0006243319,0.0004011562,0.0001395661],"domain_scores_gemma":[0.9951806,0.002159503,0.0006751497,0.001091601,0.0006929947,0.0002002276],"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":[0.0001790268,0.00003346726,0.003595339,0.002614878,0.0001258105,0.0000492079,0.00004774281,0.0008658176,0.0001504309,0.004191997,0.9656298,0.02251656],"study_design_scores_gemma":[0.0001305919,0.00002085327,0.003610404,0.0007379844,0.0000577759,0.0001336876,0.00006040046,0.0004068205,0.0001346409,0.00501671,0.9896665,0.00002368235],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004171931,0.0006270017,0.0005859398,0.000142007,0.0000752089,0.00003931016,0.9918983,0.0004249556,0.005790081],"genre_scores_gemma":[0.002251653,0.0007648534,0.001687472,0.0003046146,0.00005374445,0.000199803,0.9912629,0.0002375126,0.00323744],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1303545,"threshold_uncertainty_score":0.4360793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02258710843549944,"score_gpt":0.3994128028180193,"score_spread":0.3768256943825199,"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."}}