{"id":"W2031038069","doi":"10.1038/jes.2012.22","title":"A Bayesian mixture modeling approach for assessing the effects of correlated exposures in case-control studies","year":2012,"lang":"en","type":"article","venue":"Journal of Exposure Science & Environmental Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Statistics; Logistic regression; Bayesian probability; Econometrics; Mathematics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01223816,0.0001931576,0.0008971537,0.0001547594,0.0002106699,0.00001175636,0.0003499511,0.000135485,0.000005734324],"category_scores_gemma":[0.02024197,0.0001113444,0.000166364,0.00018442,0.0009419766,0.0003727829,0.00007190425,0.0004218182,2.08475e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001469811,"about_ca_system_score_gemma":0.00004976658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000498281,"about_ca_topic_score_gemma":7.739508e-7,"domain_scores_codex":[0.9965655,0.00116794,0.001256165,0.0002116663,0.000251599,0.0005470924],"domain_scores_gemma":[0.9836508,0.0150178,0.0009213797,0.0002191712,0.00004845432,0.0001424061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000355888,0.001831225,0.7843869,0.001118864,0.000524823,0.0002258442,0.01237875,0.008113304,0.07034717,0.0641842,0.0002258397,0.05630717],"study_design_scores_gemma":[0.005354356,0.003060386,0.09366748,0.0009502504,0.001105404,0.007891933,0.01668658,0.2683559,0.003135387,0.5989162,0.00001902892,0.0008571451],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3535661,0.003008235,0.6427495,0.00007453657,0.0003140726,0.0002621974,0.000005013247,0.000002794343,0.00001756473],"genre_scores_gemma":[0.6504307,0.00005808827,0.3493051,0.00009805713,0.00008590615,0.00001155688,2.489136e-7,0.000008547116,0.000001805543],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6907194,"threshold_uncertainty_score":0.9880109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08603609547104074,"score_gpt":0.4115833018628419,"score_spread":0.3255472063918011,"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."}}