{"id":"W4236443039","doi":"10.1515/iupac.79.0916","title":"Bioequivalence","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Bioequivalence; Hazard; Computer science; Toxicology; Medicine; Chemistry; Pharmacology; Philosophy; Biology; Linguistics; Organic chemistry","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":[0.004164433,0.00153601,0.00303078,0.004034066,0.0004143305,0.002420918,0.002584368,0.001803851,0.1386003],"category_scores_gemma":[0.0340229,0.0005610147,0.004447428,0.006063341,0.0003192267,0.002197779,0.001309311,0.002689296,0.05668263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001882222,"about_ca_system_score_gemma":0.002852744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006316839,"about_ca_topic_score_gemma":0.008264966,"domain_scores_codex":[0.994768,0.001022075,0.001344671,0.001536718,0.001139365,0.0001891675],"domain_scores_gemma":[0.9850585,0.007256199,0.002697436,0.002254849,0.002409789,0.0003232116],"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":[0.001781016,0.0001430636,0.005760198,0.01449334,0.001663595,0.00005678906,0.00003544248,0.001876943,0.0002970765,0.003260506,0.9238731,0.04675888],"study_design_scores_gemma":[0.00231399,0.0001998713,0.01228294,0.003049308,0.001199383,0.000247131,0.00003265798,0.001055733,0.0004184273,0.006938503,0.9721851,0.00007694019],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002969016,0.0008821494,0.0003643085,0.0001063381,0.00004367621,0.00009658796,0.9955769,0.0001803312,0.00245282],"genre_scores_gemma":[0.003635703,0.001196614,0.001629926,0.0004928706,0.00008458513,0.0007890689,0.9891656,0.0001828662,0.002822687],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1386003,"threshold_uncertainty_score":0.4636643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0170222970903626,"score_gpt":0.401373472207072,"score_spread":0.3843511751167095,"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."}}