{"id":"W4240879014","doi":"10.1515/iupac.79.1605","title":"Metabolic Model","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; Computer science; Multidisciplinary approach; Hazard; Toxicology; Chemistry; Biology; Philosophy; Linguistics; Sociology; Social science","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":[0.0008275408,0.002748143,0.001307752,0.00206016,0.0006059729,0.002606093,0.003361077,0.001988767,0.1098807],"category_scores_gemma":[0.00633561,0.0006145937,0.003492356,0.003453164,0.0002667211,0.001808725,0.001108596,0.001940503,0.1015867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001754494,"about_ca_system_score_gemma":0.002422817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02583058,"about_ca_topic_score_gemma":0.04321159,"domain_scores_codex":[0.9991185,0.0001677263,0.00008399341,0.0004229133,0.0001189932,0.00008794865],"domain_scores_gemma":[0.9985429,0.000574435,0.000094297,0.0003913493,0.0003188129,0.00007823083],"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":[0.0004506516,0.00008525598,0.004260461,0.001531936,0.0002455868,0.00008732223,0.00002436906,0.01432912,0.000258523,0.004500619,0.9483063,0.02591976],"study_design_scores_gemma":[0.0007142777,0.00009545853,0.004597088,0.0005779225,0.0002471592,0.000222945,0.00009095774,0.03143102,0.0006073943,0.01995495,0.94138,0.00008064406],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005956573,0.0003439735,0.00133728,0.0002484483,0.00009548318,0.00004142914,0.9926648,0.001714387,0.002958548],"genre_scores_gemma":[0.002359042,0.0002957127,0.002540916,0.0001700999,0.00002041338,0.0001412094,0.9922544,0.0001691578,0.002049102],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1098807,"threshold_uncertainty_score":0.3675877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01700665014712322,"score_gpt":0.3890584320947796,"score_spread":0.3720517819476564,"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."}}