{"id":"W4250845685","doi":"10.1515/iupac.79.1643","title":"Morbidity","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001374108,0.000907691,0.001148661,0.0004832504,0.0001645386,0.0001059318,0.001202125,0.0007612742,0.02384004],"category_scores_gemma":[0.001836819,0.0006805469,0.0003624746,0.0004082678,0.0003808375,0.0001895302,0.0004880957,0.0009807658,0.0004171425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001718603,"about_ca_system_score_gemma":0.001938931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004909058,"about_ca_topic_score_gemma":0.004329105,"domain_scores_codex":[0.994156,0.0002442234,0.0007485618,0.001032516,0.002864653,0.0009540282],"domain_scores_gemma":[0.9952999,0.0001580009,0.0005931038,0.002501097,0.001022826,0.000425051],"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":[0.0002729135,0.0003444285,0.000007517402,0.0001218817,0.0001849603,0.0002197162,0.0000034329,5.964301e-7,0.000037592,0.000007108471,0.9978418,0.0009580403],"study_design_scores_gemma":[0.001411252,0.0001677985,0.00005323598,0.0004817358,0.0002456463,0.00003625756,0.000005722472,0.000001611769,0.00004178458,0.0002143765,0.9964458,0.0008947265],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000823556,0.0006392393,0.00004373661,0.0003185255,0.001597469,0.0004653659,0.99633,0.0003824566,0.0001409106],"genre_scores_gemma":[0.000006637928,0.0005375086,0.0000464814,0.0002130577,0.002960372,0.00002583931,0.9951368,0.0002370438,0.0008362642],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0234229,"threshold_uncertainty_score":0.9995646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02520606678838802,"score_gpt":0.4342012545281012,"score_spread":0.4089951877397132,"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."}}