{"id":"W4254672946","doi":"10.1515/iupac.79.1201","title":"Endemic","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Toxicology; Library science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001557911,0.00160639,0.001429497,0.003935916,0.001112191,0.003781107,0.002703043,0.001879957,0.1957582],"category_scores_gemma":[0.01459137,0.0005656467,0.001883706,0.007023256,0.0003772464,0.002597547,0.002232492,0.001845158,0.2190986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001858115,"about_ca_system_score_gemma":0.003334543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02172787,"about_ca_topic_score_gemma":0.03513227,"domain_scores_codex":[0.9971812,0.0004762554,0.0004811686,0.0009930168,0.0005625673,0.000305906],"domain_scores_gemma":[0.9941742,0.001411039,0.0006141572,0.001425973,0.002019752,0.0003549532],"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":[0.00008801226,0.00001519558,0.001472349,0.0007846435,0.00003611392,0.0000171303,0.00002420012,0.0001190002,0.00005835086,0.0008796599,0.9898847,0.006620679],"study_design_scores_gemma":[0.0001271994,0.00001386649,0.003416947,0.000605798,0.00003936041,0.00005918967,0.00009235764,0.0001654995,0.0001328596,0.001558321,0.993766,0.00002274075],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001194523,0.0001175403,0.0001109375,0.0001224727,0.0000499997,0.00002542692,0.9971215,0.0002020678,0.002130618],"genre_scores_gemma":[0.0004642988,0.0001195964,0.0003721938,0.0002006048,0.00002005246,0.0001436139,0.9965365,0.0000792291,0.002063971],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1957582,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01824628863868082,"score_gpt":0.4242094555538371,"score_spread":0.4059631669151563,"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."}}