{"id":"W3086730566","doi":"10.1093/annweh/wxaa086","title":"Evidence of Absence: Bayesian Way to Reveal True Zeros Among Occupational Exposures","year":2020,"lang":"en","type":"article","venue":"Annals of Work Exposures and Health","topic":"Occupational and environmental lung diseases","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Log-normal distribution; Statistics; Censoring (clinical trials); Bayesian probability; Econometrics; Mathematics; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0002311907,0.0001206609,0.0003482498,0.00005116437,0.00007017075,0.000007010514,0.00007429666,0.00004522091,0.0001245059],"category_scores_gemma":[0.0001085685,0.0001016099,0.00008645732,0.0001902912,0.0001049701,0.00008791406,0.000046242,0.00007995145,0.000005813365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001086573,"about_ca_system_score_gemma":0.00009409517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000196385,"about_ca_topic_score_gemma":0.00001186974,"domain_scores_codex":[0.9986686,0.00004274461,0.0004664096,0.0002353722,0.0003890597,0.0001978003],"domain_scores_gemma":[0.9990489,0.00008435276,0.0001788149,0.0001408892,0.00006421156,0.0004828125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001188151,0.0001073015,0.966497,0.000480081,0.00003117942,0.000003118945,0.000665882,0.00005191142,0.0004482235,0.0001721033,0.02233331,0.008021764],"study_design_scores_gemma":[0.0002529506,0.001846313,0.9952913,0.0008743634,0.00001342709,0.000001380557,0.0001337026,0.00001525042,0.0011055,0.00009610582,0.0002876241,0.00008208125],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973041,0.004733269,0.0001105934,0.02159674,0.00003120006,0.0003373495,0.00004665047,0.0000138922,0.0000893001],"genre_scores_gemma":[0.990841,0.0009988128,0.0009673498,0.006921912,0.0001486106,0.00001172176,0.0000253124,0.00001037216,0.00007488686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02879433,"threshold_uncertainty_score":0.4143533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1416045857293761,"score_gpt":0.3799151005925978,"score_spread":0.2383105148632216,"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."}}