{"id":"W4231780036","doi":"10.1515/iupac.79.1850","title":"Prevalence","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; Hazard; Computer science; Multidisciplinary approach; Toxicology; Library science; Chemistry; Philosophy; Biology; Linguistics; Sociology; Social science; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00160883,0.001545688,0.001827038,0.004341409,0.001091651,0.003929151,0.003228828,0.001997759,0.2181037],"category_scores_gemma":[0.01874969,0.0006136897,0.002024349,0.007412407,0.0003966639,0.003351271,0.002152592,0.002170785,0.1636633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001765028,"about_ca_system_score_gemma":0.003105316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02878206,"about_ca_topic_score_gemma":0.03731355,"domain_scores_codex":[0.9969668,0.0004797009,0.0005635874,0.001086627,0.0005786802,0.0003246788],"domain_scores_gemma":[0.9931725,0.001857705,0.0009458393,0.001161018,0.002533436,0.0003294823],"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":[0.0001062314,0.000019017,0.003357875,0.001227309,0.0000544797,0.00002483602,0.00004291158,0.00009724559,0.00004174531,0.001243492,0.9843603,0.009424617],"study_design_scores_gemma":[0.0002104881,0.00002158794,0.008763314,0.001201907,0.00009093362,0.000134613,0.0002058652,0.0002270889,0.0001189081,0.002471948,0.9865116,0.00004167554],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002660431,0.000276462,0.0001586433,0.0002173641,0.00008719762,0.0000474467,0.9952387,0.0001846729,0.003523482],"genre_scores_gemma":[0.001623339,0.0003692489,0.0006951691,0.0004309481,0.00007143401,0.0004518378,0.9911118,0.0001393084,0.005106851],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7818963,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01686200186927288,"score_gpt":0.4203341267873163,"score_spread":0.4034721249180434,"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."}}