{"id":"W4245572006","doi":"10.1515/iupac.76.0316","title":"Necrosis","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Chemical Safety and Risk Management","field":"Chemical Engineering","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; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002219669,0.0004672672,0.0005857281,0.00009800831,0.00007013266,0.00003505863,0.0005977184,0.0004679265,0.008821065],"category_scores_gemma":[0.0003727499,0.000334322,0.0002767519,0.0001735288,0.00009046614,0.00006081312,0.0003985716,0.0005925994,0.00001822244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005455146,"about_ca_system_score_gemma":0.00009826814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000136757,"about_ca_topic_score_gemma":0.00008797713,"domain_scores_codex":[0.9975376,0.00001836105,0.0004815108,0.000532517,0.0008962909,0.0005336887],"domain_scores_gemma":[0.9985167,0.0001132848,0.00015001,0.0008493996,0.0001470493,0.0002235903],"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":[0.00007700995,0.0001298644,4.646965e-7,0.0002501681,0.0001372617,0.00003042124,0.000002906054,0.00001369926,0.0003729165,0.00003009629,0.9947111,0.004244065],"study_design_scores_gemma":[0.0007058377,0.00003550567,0.000001778201,0.0004304529,0.000134963,0.000002836872,0.000003283739,0.00004512896,0.0006192303,0.0002012135,0.9973102,0.0005095302],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001402063,0.0004711232,0.0008932495,0.0009121119,0.0005517944,0.0001783051,0.9965692,0.0001833652,0.0002268234],"genre_scores_gemma":[0.00001066298,0.001717642,0.00006014248,0.0002810073,0.001148566,0.00001784675,0.994424,0.00005160653,0.002288495],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.008802842,"threshold_uncertainty_score":0.9999109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01004593723858015,"score_gpt":0.3475362443333194,"score_spread":0.3374903070947393,"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."}}