{"id":"W4249550597","doi":"10.1515/iupac.76.0106","title":"Active Metabolite","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Cancer Treatment and Pharmacology","field":"Medicine","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; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009396425,0.001655616,0.001629725,0.003173325,0.0005687071,0.002065526,0.001871479,0.001357343,0.111338],"category_scores_gemma":[0.006745872,0.0005766599,0.001729584,0.005151638,0.0003091036,0.00155302,0.00143323,0.001586731,0.1028238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001120424,"about_ca_system_score_gemma":0.002245406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008944402,"about_ca_topic_score_gemma":0.01777202,"domain_scores_codex":[0.9987885,0.0002009246,0.0002675638,0.0004018554,0.0002521515,0.00008896302],"domain_scores_gemma":[0.9974268,0.0008810838,0.0004829714,0.0005065437,0.000540861,0.0001616838],"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":[0.0003687937,0.00005185678,0.002066426,0.005359654,0.000125964,0.000064429,0.00003446104,0.0004094735,0.0005288523,0.0008919371,0.9729074,0.0171907],"study_design_scores_gemma":[0.0002138586,0.00003608275,0.003812727,0.0007700521,0.00009004156,0.0001216814,0.00003103257,0.0001621492,0.0003838618,0.001070066,0.9932829,0.00002561243],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001311372,0.0003613358,0.0001164105,0.00004529845,0.00002342814,0.00002551389,0.9980742,0.0001891205,0.001033507],"genre_scores_gemma":[0.0004538039,0.0003486597,0.0005068481,0.0001291045,0.00001168753,0.000136377,0.9973059,0.00006697317,0.00104067],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.111338,"threshold_uncertainty_score":0.3724627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02006685103399882,"score_gpt":0.4712057248539894,"score_spread":0.4511388738199906,"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."}}