{"id":"W4242485413","doi":"10.1515/iupac.76.0410","title":"Toxic","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; Toxicokinetics; Hazard; Relation (database); Toxicology; Computer science; Medicine; Chemistry; Pharmacology; Data mining; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000171897,0.0005196076,0.0005460225,0.00005788909,0.0001515241,0.0000299802,0.0007233815,0.0005795132,0.08665763],"category_scores_gemma":[0.0003820155,0.0004335088,0.0001972565,0.00007179756,0.0002087915,0.00007895307,0.0001910781,0.0006868425,0.00001075757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009430831,"about_ca_system_score_gemma":0.0005835932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007023245,"about_ca_topic_score_gemma":0.00003603568,"domain_scores_codex":[0.9973973,0.00001326628,0.000488087,0.0006532401,0.0009638318,0.0004842587],"domain_scores_gemma":[0.9979045,0.00007760173,0.000309473,0.001306654,0.0001919261,0.0002098311],"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":[0.00007776661,0.000201169,0.000001490221,0.0006272912,0.00009137519,0.0001199054,0.000003883858,4.315137e-7,0.003169141,0.000001769682,0.9918798,0.003826029],"study_design_scores_gemma":[0.0007421129,0.00001993623,7.225937e-8,0.0007360398,0.00008257548,0.00001774515,0.00001328797,2.643814e-7,0.007746804,0.00008794036,0.9899968,0.0005564283],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008184349,0.0009724155,0.00002829713,0.00009683287,0.0006459205,0.00004456818,0.9950755,0.0001095756,0.00294503],"genre_scores_gemma":[0.000008229062,0.0007908016,0.00004018197,0.0001727594,0.002091132,0.00001757713,0.9779865,0.00005172323,0.01884115],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08664687,"threshold_uncertainty_score":0.9998116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0132825728928052,"score_gpt":0.3875141240083136,"score_spread":0.3742315511155084,"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."}}