{"id":"W4241122649","doi":"10.1515/iupac.76.0303","title":"Michaelis–Menten Kinetics","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; Hazard; Multidisciplinary approach; Computer science; Toxicology; Medicine; Chemistry; Pharmacology; Biology; Philosophy; Political science; Linguistics; Law","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":[0.002012875,0.002806895,0.002241252,0.003477712,0.0009274473,0.004735483,0.005170926,0.002508183,0.1477495],"category_scores_gemma":[0.01269748,0.001183709,0.002653005,0.006652699,0.0003599512,0.00358034,0.001777786,0.003217636,0.2230984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001826478,"about_ca_system_score_gemma":0.0031947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01198181,"about_ca_topic_score_gemma":0.02011654,"domain_scores_codex":[0.9977493,0.000424137,0.0003267025,0.0007496239,0.0005990904,0.0001511255],"domain_scores_gemma":[0.9959351,0.001719475,0.0003573246,0.001156889,0.0006687121,0.0001625598],"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":[0.00008970971,0.00004988062,0.0008584682,0.001674409,0.00007736831,0.00003156329,0.00002243609,0.003234921,0.0001677154,0.004604771,0.9747711,0.01441764],"study_design_scores_gemma":[0.0002425976,0.00002465015,0.001061538,0.0003432333,0.00004160933,0.0000733757,0.00002543704,0.006406061,0.0004309273,0.01242767,0.9788715,0.0000513887],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002740109,0.0007105665,0.002459549,0.0001871497,0.00008381051,0.00005225268,0.9873072,0.003686341,0.005239242],"genre_scores_gemma":[0.00159436,0.0009789852,0.005761011,0.0001804773,0.00003158578,0.0003522169,0.9847568,0.000944046,0.005400511],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1477495,"threshold_uncertainty_score":0.4942712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01290029678463087,"score_gpt":0.3777481259970432,"score_spread":0.3648478292124123,"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."}}