{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003603253,0.0001984992,0.0003520679,0.000258129,0.0001260095,0.0001265971,0.0002339771,0.0001355474,0.00009932552],"category_scores_gemma":[0.008191547,0.0001715641,0.0001007081,0.002136172,0.0000774649,0.0001390493,0.00009712625,0.0001257271,0.00001139318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003379305,"about_ca_system_score_gemma":0.00005329336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001148541,"about_ca_topic_score_gemma":0.000001207853,"domain_scores_codex":[0.998552,0.00005020191,0.0003893274,0.0003716907,0.0002575978,0.0003792318],"domain_scores_gemma":[0.9960658,0.003064286,0.0001377503,0.0002582093,0.0001935719,0.0002803705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003912899,0.00007524664,0.00006521228,0.0003796442,0.00002608066,0.0001047703,0.0002793937,0.000002885654,0.0001853981,0.9336553,0.002541492,0.06264549],"study_design_scores_gemma":[0.0004875146,0.0001682234,0.000009031626,0.00001348353,0.00004344465,0.00003886511,0.00005716897,0.1949811,0.0005291642,0.8023452,0.001074423,0.000252374],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007208113,0.00006351687,0.9963376,0.0003257836,0.0001223746,0.0004401551,0.0004098572,0.0001341712,0.001445732],"genre_scores_gemma":[0.2576557,0.00001053898,0.7417976,0.0003212598,0.0001045722,0.00004195176,0.000006238975,0.00002785923,0.00003425913],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2569349,"threshold_uncertainty_score":0.9806639,"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."}}