{"id":"W1971972523","doi":"10.1002/sim.1463","title":"Hierarchical Bayesian spatial modelling of small‐area rates of non‐rare disease","year":2003,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"British Columbia Centre of Excellence for Women's Health; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Bayesian probability; Computer science; Bayesian hierarchical modeling; Statistics; Hierarchical database model; Bayesian inference; Econometrics; Artificial intelligence; Data mining; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003929634,0.0001018416,0.0002813677,0.00005879333,0.00002140154,8.927317e-7,0.00009322482,0.00008688434,0.00006072795],"category_scores_gemma":[0.001348186,0.00008865331,0.00002762315,0.00007733908,0.000200978,6.263485e-7,0.0000243999,0.00008973055,4.041173e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008210692,"about_ca_system_score_gemma":0.0001100717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001365289,"about_ca_topic_score_gemma":0.0001272003,"domain_scores_codex":[0.9989723,0.0001304174,0.000433265,0.0001899193,0.0001007122,0.0001733851],"domain_scores_gemma":[0.99933,0.0001147232,0.000150014,0.0002047538,0.000110438,0.00009001185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004500901,0.0004883297,0.8463632,0.0004991075,0.0002113012,0.00004393671,0.0007953956,0.08513493,0.03038149,0.02132535,0.005542642,0.008764174],"study_design_scores_gemma":[0.006904178,0.002712036,0.3766034,0.0005043757,0.0003048129,0.00001194132,0.0009585574,0.4907517,0.01035872,0.1065648,0.003444435,0.000880998],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1526718,0.0003459275,0.8458419,0.0001183942,0.00009537124,0.0001048495,0.000135566,0.000001243098,0.0006849857],"genre_scores_gemma":[0.9321472,0.0002335914,0.06710029,0.00007551814,0.00005716848,0.00000728665,0.0002546639,0.00001027021,0.0001139834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7794755,"threshold_uncertainty_score":0.3615177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02524430431112855,"score_gpt":0.3017554795147386,"score_spread":0.2765111752036101,"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."}}