{"id":"W4254809073","doi":"10.1515/iupac.79.0774","title":"Adrenergic","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Chemistry and Chemical Engineering","field":"Environmental Science","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","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001123295,0.0002832137,0.0002667805,0.00001624559,0.00004384253,0.00001752091,0.0004136591,0.0002646352,0.0445027],"category_scores_gemma":[0.0001267581,0.0002166927,0.0001065207,0.0001069091,0.0001296242,0.00005193957,0.0003069022,0.0002953689,0.00001885704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004566566,"about_ca_system_score_gemma":0.00003738144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005771764,"about_ca_topic_score_gemma":0.00005683958,"domain_scores_codex":[0.9984097,0.00000798207,0.0002254519,0.0003858802,0.0006487138,0.000322231],"domain_scores_gemma":[0.9991812,0.00003115291,0.00007701854,0.000522187,0.00001239401,0.0001760566],"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":[0.00001453176,0.00005161332,0.000003889077,0.00003528277,0.00001354667,0.00002791884,0.000001164487,0.00005124122,0.001775631,4.643742e-7,0.9969136,0.001111115],"study_design_scores_gemma":[0.0002578139,0.00001817793,0.000007589415,0.00009360377,0.00002765789,0.00001190814,0.00000139655,0.00001013602,0.002643068,0.00005196316,0.9965619,0.0003147225],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005241039,0.0000932589,0.00005219116,0.000103081,0.0001846204,0.00005036133,0.9982136,0.00005378218,0.0007249835],"genre_scores_gemma":[0.0001520891,0.0001930489,0.00002966464,0.0001033248,0.0004339739,0.000006712437,0.9980081,0.00002022496,0.001052851],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04448384,"threshold_uncertainty_score":0.9563708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005302612187823427,"score_gpt":0.3176334132047971,"score_spread":0.3123308010169737,"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."}}