{"id":"W3043464877","doi":"10.1111/biom.13329","title":"Approximate Bayesian inference for case‐crossover models","year":2020,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Centre for Global Health Research; St. Michael's Hospital","funders":"","keywords":"Crossover; Inference; Flexibility (engineering); Computer science; Laplace's method; Bayesian probability; Econometrics; Statistics; Mathematics; Artificial intelligence","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03068618,0.001342191,0.003515076,0.002994242,0.001246712,0.003052921,0.005730181,0.003023915,0.01331525],"category_scores_gemma":[0.1169636,0.001931959,0.002958014,0.003984306,0.002691942,0.004512032,0.002847125,0.005491298,0.001802791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002863739,"about_ca_system_score_gemma":0.002546421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01469306,"about_ca_topic_score_gemma":0.01492583,"domain_scores_codex":[0.9894678,0.006956303,0.0004628846,0.001609388,0.001143446,0.0003602096],"domain_scores_gemma":[0.9193925,0.07183773,0.002485779,0.003909721,0.001843983,0.0005303078],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001873884,0.0001238331,0.004167601,0.0003875699,0.0004779696,0.000355472,0.0004123351,0.414724,0.0003773545,0.500677,0.006967993,0.07114138],"study_design_scores_gemma":[0.00004667139,0.00001965545,0.0005084269,0.00005488875,0.00005726358,0.00007253508,0.00003323033,0.6635988,0.0001052605,0.3330667,0.002412016,0.00002453857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003772422,0.000342021,0.9938005,0.0003212171,0.00003654003,0.00007532854,0.0003940848,0.0002494942,0.001008475],"genre_scores_gemma":[0.251596,0.001961506,0.7299642,0.0007207799,0.0005425111,0.001766906,0.004115316,0.0004655866,0.008867165],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9693138,"threshold_uncertainty_score":0.162286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2269703437760123,"score_gpt":0.4138689920203854,"score_spread":0.1868986482443731,"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."}}