{"id":"W2147497323","doi":"10.1002/sim.4176","title":"Bayesian inference of gene–environment interaction from incomplete data: What happens when information on environment is disjoint from data on gene and disease?","year":2011,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Environmental data; Inference; Mendelian randomization; Population; Disjoint sets; Computer science; Disease; Bayesian probability; Genotype; Statistics; Econometrics; Biology; Genetics; Mathematics; Medicine; Environmental health; Gene; Artificial intelligence; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002848193,0.0001521825,0.0002359866,0.00004961644,0.00003727292,0.000008889264,0.000317486,0.0000806419,0.0002909274],"category_scores_gemma":[0.000453873,0.0001330137,0.000009618576,0.00001655299,0.0001487127,0.00002915482,0.000371411,0.0001098456,0.00001780901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003407378,"about_ca_system_score_gemma":0.00002688741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001162448,"about_ca_topic_score_gemma":0.00009895657,"domain_scores_codex":[0.9986891,0.000105327,0.000475907,0.0004017339,0.0001833581,0.0001445431],"domain_scores_gemma":[0.998385,0.0001361619,0.0002724699,0.001078075,0.00001431053,0.0001140082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002695133,0.001507606,0.4761183,0.0001679676,0.001248217,0.00004666159,0.01330056,0.00246156,0.03612545,0.002444553,0.09499152,0.3688925],"study_design_scores_gemma":[0.002026503,0.001118543,0.9106936,0.000232583,0.0002141507,0.00000115766,0.001351975,0.03446182,0.001819141,0.02015946,0.02748817,0.0004328772],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2504086,0.0007606408,0.7304223,0.001558997,0.0004006803,0.0004217416,0.01580566,0.000005374829,0.0002160208],"genre_scores_gemma":[0.903231,0.005565423,0.05581184,0.001319075,0.0001361983,0.00001148567,0.0338989,0.00001179984,0.00001424924],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6746104,"threshold_uncertainty_score":0.5424143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05688999045053923,"score_gpt":0.2997278995458502,"score_spread":0.242837909095311,"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."}}