{"id":"W2149281073","doi":"10.1111/j.0006-341x.2004.00241.x","title":"Estimation in Bayesian Disease Mapping","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayes' theorem; Inference; Bayesian inference; Markov chain Monte Carlo; Bayesian probability; Statistical inference; Computer science; Statistics; Bayes factor; Fiducial inference; Econometrics; Parametric statistics; Frequentist inference; Mathematics; Artificial intelligence","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":[0.0003581262,0.0000927919,0.0001437193,0.000926501,0.00003601315,0.00003865777,0.0001089111,0.00004902933,0.00004616954],"category_scores_gemma":[0.006259717,0.00008513885,0.00003165819,0.003146674,0.00003421304,0.00007148069,0.00003026223,0.0000807804,0.00002869111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000116278,"about_ca_system_score_gemma":0.00005735416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002023755,"about_ca_topic_score_gemma":0.000002354739,"domain_scores_codex":[0.9991285,0.0000382236,0.0002526981,0.0001699318,0.0002150325,0.000195626],"domain_scores_gemma":[0.9990093,0.000583747,0.00006167957,0.000182174,0.00003177921,0.0001313589],"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":[0.000007094668,0.0001667091,0.002437316,0.0001247117,0.000004248077,0.00004662008,0.0001134573,0.00001116944,0.00007519971,0.7810608,0.00007153343,0.2158811],"study_design_scores_gemma":[0.0003248456,0.00001990676,0.02915158,0.00007164753,0.000007536297,0.000001401291,0.00001966469,0.00459824,0.0000918164,0.9654801,0.0001102042,0.0001230525],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0097165,0.00005708799,0.988862,0.0002487177,0.0001059629,0.0001370158,0.00001616426,0.00005181886,0.0008047112],"genre_scores_gemma":[0.3975606,0.000006362132,0.6023508,0.00003796471,0.00001543686,0.000007749665,0.000002773519,0.000007470324,0.0000108974],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3878441,"threshold_uncertainty_score":0.7493917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08734313465568595,"score_gpt":0.3776616522745233,"score_spread":0.2903185176188374,"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."}}