{"id":"W2097109066","doi":"10.1111/j.1365-2885.2012.01376.x","title":"Estimating product bioequivalence for highly variable veterinary drugs","year":2012,"lang":"en","type":"article","venue":"Journal of Veterinary Pharmacology and Therapeutics","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Bioequivalence; Veterinary Drugs; Pharmacology; Veterinary drug; Medicine; Product (mathematics); Veterinary medicine; Mathematics; Pharmacokinetics; Chemistry; Chromatography","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":[],"consensus_categories":[],"category_scores_codex":[0.001333729,0.0001879731,0.0003749974,0.0000998241,0.0002042947,0.00001244582,0.0002037537,0.0001895439,0.0001785677],"category_scores_gemma":[0.0000302988,0.0001425305,0.000106947,0.0000990638,0.0003029422,0.000230355,0.00009049392,0.0003833202,0.00000484471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000258664,"about_ca_system_score_gemma":0.0000478375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.569821e-7,"about_ca_topic_score_gemma":7.413361e-9,"domain_scores_codex":[0.9985634,0.0003456117,0.0004525625,0.0001535856,0.00003794813,0.0004468789],"domain_scores_gemma":[0.9988912,0.0004865637,0.0003285161,0.0001010788,0.0001283868,0.00006425255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001464038,0.0003512084,0.0004662969,0.0001157026,0.0006363015,0.000009581067,0.0003115326,0.000003778756,0.9638658,0.0004453339,0.0008997971,0.0314306],"study_design_scores_gemma":[0.004264114,0.009370482,0.001530659,0.0000964752,0.001544924,0.005996761,0.0001839913,0.0003093999,0.1430477,0.001357541,0.8318025,0.0004954477],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9504983,0.03094583,0.007820661,0.001738365,0.008339896,0.000322291,0.00003361602,0.00002840907,0.0002726215],"genre_scores_gemma":[0.942259,0.0007145414,0.05447275,0.001602856,0.0006439057,0.000009649983,0.000002561284,0.00001847251,0.0002762626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8309027,"threshold_uncertainty_score":0.5812226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08166669538535094,"score_gpt":0.3881280151996901,"score_spread":0.3064613198143392,"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."}}