{"id":"W4229977045","doi":"10.1515/iupac.76.0167","title":"Chronic Effect","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; Relation (database); Hazard; Multidisciplinary approach; Computer science; Toxicology; Medicine; Pharmacology; Chemistry; Data mining; Biology; Linguistics; Political science; Philosophy; 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":[],"consensus_categories":[],"category_scores_codex":[0.001149956,0.001118425,0.001344669,0.003017348,0.0006697751,0.00201724,0.001539815,0.001145579,0.1350989],"category_scores_gemma":[0.01137194,0.0003666231,0.002252149,0.00490501,0.0002829013,0.001737999,0.001297123,0.001349251,0.05391778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001218919,"about_ca_system_score_gemma":0.002197057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01505184,"about_ca_topic_score_gemma":0.02732077,"domain_scores_codex":[0.9984805,0.0002336738,0.0003801907,0.0005086719,0.0002684544,0.0001285931],"domain_scores_gemma":[0.9951894,0.001523486,0.001116204,0.0007804293,0.001104853,0.0002855342],"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":[0.0005189245,0.00004966095,0.01196072,0.004737384,0.0002506016,0.00006157249,0.00007276964,0.0002476903,0.0001245596,0.001746435,0.9557058,0.02452381],"study_design_scores_gemma":[0.0002786885,0.00006518543,0.03325426,0.002370944,0.0003033789,0.0002531489,0.0001629943,0.0001664587,0.0001949638,0.002587138,0.9603127,0.00004999436],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005543384,0.0008587342,0.0001495096,0.0001428394,0.00008274591,0.00005084,0.994676,0.00009882056,0.003386143],"genre_scores_gemma":[0.003408209,0.001227928,0.0007116185,0.0004194657,0.0001056276,0.0005089674,0.9874052,0.00009412025,0.006118817],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1350989,"threshold_uncertainty_score":0.4519508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646974488787148,"score_gpt":0.3964219301450304,"score_spread":0.3799521852571589,"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."}}