{"id":"W4251555258","doi":"10.1515/iupac.76.0304","title":"Michaels–Menten Mechanism","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Digital Innovation in Industries","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; Hazard; Relation (database); Multidisciplinary approach; Computer science; Toxicology; Medicine; Chemistry; Pharmacology; Data mining; Biology; Linguistics; Political science; Philosophy; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005524414,0.0005703827,0.0005763628,0.0007203664,0.0001804363,0.0007626871,0.000810541,0.0004457585,0.007249359],"category_scores_gemma":[0.0007660721,0.0004571522,0.0001501509,0.0006660859,0.0001599264,0.001269404,0.0006516814,0.0004469115,0.00009076422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002577996,"about_ca_system_score_gemma":0.0002563821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002245494,"about_ca_topic_score_gemma":0.0002246044,"domain_scores_codex":[0.996823,0.000008582143,0.0007129369,0.0005953794,0.001319337,0.000540825],"domain_scores_gemma":[0.9970939,0.00004744803,0.0007206069,0.0007990544,0.001314044,0.00002493332],"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":[0.00005193356,0.0001212291,0.000009646454,0.0001842014,0.00009351031,0.00003865981,0.000001092703,2.638667e-7,0.000005884353,0.00802236,0.9893148,0.002156422],"study_design_scores_gemma":[0.0006621569,0.00002185033,0.000006662023,0.0004312723,0.00009766117,0.000003860602,0.00003846931,0.000003869976,0.00001626731,0.0117676,0.9863751,0.0005752175],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007431145,0.00006397587,0.0002154492,0.003025798,0.002658118,0.0003590243,0.9908554,0.0002298915,0.002518021],"genre_scores_gemma":[0.00005364544,0.00004220436,0.00001998889,0.004237683,0.005782281,0.00003417437,0.9865403,0.00007599546,0.003213693],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.007158594,"threshold_uncertainty_score":0.999788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02464424577317776,"score_gpt":0.351646503033124,"score_spread":0.3270022572599462,"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."}}