{"id":"W4237475455","doi":"10.1515/iupac.79.1608","title":"Metabolite","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; Forensic toxicology; Computer science; Toxicology; Chemistry; Biology; Philosophy; 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":[0.001342029,0.00200192,0.001512989,0.003031937,0.0009037585,0.003194547,0.002856936,0.001863974,0.1608435],"category_scores_gemma":[0.009431753,0.000569372,0.001943239,0.004410647,0.000367167,0.002181128,0.002045297,0.001742354,0.2237862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00141536,"about_ca_system_score_gemma":0.00263243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01204755,"about_ca_topic_score_gemma":0.02644036,"domain_scores_codex":[0.998161,0.0002991552,0.0002737663,0.0007107593,0.0003651204,0.0001902076],"domain_scores_gemma":[0.9967037,0.0008060447,0.00033651,0.0009048558,0.0009872231,0.0002616794],"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":[0.0001630284,0.0000270037,0.001440305,0.001080223,0.00004967341,0.00002426957,0.00001958887,0.0002102061,0.0001256341,0.0006759042,0.9880314,0.008152689],"study_design_scores_gemma":[0.0001924615,0.00002469466,0.003059377,0.0004581925,0.00004889542,0.0000726371,0.00005743503,0.0002586472,0.0002666384,0.001600044,0.9939353,0.00002563551],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001285076,0.0001504332,0.0001229697,0.00009665509,0.00004706547,0.00002685774,0.9973278,0.0003722789,0.001727488],"genre_scores_gemma":[0.000360455,0.000103564,0.0003825074,0.0001471731,0.00001290074,0.0001048068,0.9975559,0.00007120023,0.00126141],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1608435,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01283063056382127,"score_gpt":0.3903427331121932,"score_spread":0.3775121025483719,"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."}}