{"id":"W4232581202","doi":"10.1515/iupac.76.0330","title":"Pharmacokinetics","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; Toxicokinetics; Computer science; Toxicology; Medicine; Pharmacology; Pharmacokinetics; Biology; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001821001,0.001943499,0.001979787,0.00317417,0.0006740668,0.003518292,0.002306654,0.001951949,0.1577413],"category_scores_gemma":[0.01707668,0.0007539036,0.002258017,0.004790339,0.0003399393,0.002346603,0.001692423,0.002176025,0.144011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001748363,"about_ca_system_score_gemma":0.003316488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01043671,"about_ca_topic_score_gemma":0.01629009,"domain_scores_codex":[0.9980441,0.0003746729,0.0003940811,0.0006588447,0.0004032449,0.0001250299],"domain_scores_gemma":[0.9934976,0.002811357,0.0009215198,0.001281316,0.001193661,0.0002945006],"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":[0.0003368654,0.00004901281,0.002380664,0.00498865,0.0001530212,0.00004837093,0.00003124158,0.0005852605,0.0002388611,0.001212049,0.9639327,0.02604326],"study_design_scores_gemma":[0.0002841431,0.00003846731,0.004168297,0.001234168,0.0001268501,0.0001419857,0.00002703482,0.0004085061,0.0002796066,0.003072759,0.9901764,0.00004178802],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001488147,0.0008233659,0.0003703871,0.0001449414,0.00004541767,0.00004110868,0.9954677,0.0006894702,0.002268874],"genre_scores_gemma":[0.001104129,0.0009292934,0.001341121,0.0003931054,0.00003786449,0.00029957,0.9934515,0.0002428149,0.002200629],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8422587,"threshold_uncertainty_score":0.5276972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01514519967957633,"score_gpt":0.4070110095209885,"score_spread":0.3918658098414121,"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."}}