{"id":"W4255602460","doi":"10.1515/iupac.76.0419","title":"Toxicology","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Animal testing and alternatives","field":"Veterinary","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; Relation (database); Toxicology; Computer science; Medicine; Pharmacology; Data mining; Biology; Philosophy; Linguistics","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":[0.001405937,0.00182244,0.001653851,0.005460854,0.0008850528,0.003368251,0.002535614,0.001719243,0.1434488],"category_scores_gemma":[0.009459767,0.0007340557,0.001719957,0.008409017,0.0003952365,0.002295054,0.002375671,0.001888454,0.1629447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001834377,"about_ca_system_score_gemma":0.003146636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01359477,"about_ca_topic_score_gemma":0.02553782,"domain_scores_codex":[0.9981295,0.0003165029,0.0003799502,0.0005513626,0.0004733718,0.000149357],"domain_scores_gemma":[0.995833,0.001372302,0.0005726213,0.0008792188,0.001071638,0.0002712756],"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":[0.00007375465,0.00001877241,0.001027022,0.002819183,0.0000460947,0.0000367607,0.00003364593,0.0002556957,0.0002530045,0.001077072,0.984064,0.01029501],"study_design_scores_gemma":[0.00005572503,0.000009997047,0.001940207,0.000631429,0.00002865258,0.00006523545,0.00002993352,0.0001113393,0.0001579694,0.001096879,0.9958573,0.00001547924],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000782285,0.0002760288,0.0001551661,0.00007207799,0.00002772781,0.00001752007,0.9972553,0.0003253642,0.00179274],"genre_scores_gemma":[0.0002370584,0.0002816927,0.0004699642,0.0001163017,0.000009061077,0.0000747785,0.9976094,0.00009224968,0.001109446],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1434488,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1036462976646196,"score_gpt":0.5157586889485893,"score_spread":0.4121123912839698,"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."}}