{"id":"W4254948390","doi":"10.1515/iupac.79.0936","title":"Biomolecule","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; Toxicology; Chemistry; Philosophy; Biology; 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":[0.0009832456,0.002532799,0.002055827,0.003818755,0.001084831,0.00304709,0.003204336,0.002297384,0.1069921],"category_scores_gemma":[0.005587265,0.0008139876,0.00212165,0.006059113,0.0004007054,0.00183154,0.002500544,0.002180165,0.1597719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001553335,"about_ca_system_score_gemma":0.002907639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01426711,"about_ca_topic_score_gemma":0.02945581,"domain_scores_codex":[0.9989212,0.0001565807,0.0001574889,0.0003728633,0.000270622,0.0001211663],"domain_scores_gemma":[0.9983081,0.0004427743,0.0002350479,0.0004726728,0.000382118,0.0001592733],"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":[0.0001920372,0.00003683783,0.001243182,0.003644957,0.0001023416,0.00003926396,0.00002951766,0.0004720729,0.0004306222,0.000919144,0.984721,0.008169097],"study_design_scores_gemma":[0.0001858756,0.00002602541,0.002545826,0.0005338128,0.00007337574,0.00007958871,0.00003398668,0.0002989141,0.000480358,0.001445731,0.9942682,0.0000282818],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001025105,0.0003389484,0.0000885909,0.00005366929,0.00002514491,0.00001603012,0.9982198,0.0003217385,0.0008335745],"genre_scores_gemma":[0.000285113,0.0002110741,0.0003199213,0.00007642593,0.000006122391,0.00007427155,0.9983044,0.00004942148,0.0006732708],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1069921,"threshold_uncertainty_score":0.3579243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01181863399009989,"score_gpt":0.3910101294736311,"score_spread":0.3791914954835312,"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."}}