{"id":"W2082993001","doi":"10.1111/j.0006-341x.2001.00197.x","title":"Detecting Interaction Between Random Region and Fixed Age Effects in Disease Mapping","year":2001,"lang":"en","type":"article","venue":"Biometrics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Ministry of Health, British Columbia","keywords":"Interaction; Simple (philosophy); Computer science; Econometrics; Test (biology); Random effects model; Statistics; Mathematics; Medicine","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":[0.001183729,0.0001063428,0.0001803641,0.00208105,0.0002887431,0.000141481,0.0001337724,0.00007089337,0.000003299295],"category_scores_gemma":[0.001352419,0.0001122128,0.00006432361,0.005950924,0.0001029613,0.0002584374,0.00006011687,0.0001226981,0.00000626244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001425857,"about_ca_system_score_gemma":0.00001569687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001499568,"about_ca_topic_score_gemma":0.0004362328,"domain_scores_codex":[0.9985669,0.0002860713,0.0002253248,0.0002634693,0.000349905,0.0003083456],"domain_scores_gemma":[0.9989681,0.0005936367,0.0001243142,0.0001400131,0.00003833064,0.0001355899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003200668,0.00003200262,0.8691638,0.00005697846,0.00001523718,0.0001126413,0.001308595,0.000002770472,0.00001883841,0.000144176,0.0000420362,0.129071],"study_design_scores_gemma":[0.0009471592,0.00002002618,0.9762903,0.00006972876,0.00002606206,3.929811e-7,0.0008328075,0.00009032093,0.000008068778,0.0006458219,0.02091414,0.0001551033],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907988,0.0005171514,0.00528929,0.000187812,0.0004989777,0.0005414117,0.000001280851,0.00008355887,0.002081657],"genre_scores_gemma":[0.9986537,0.000699651,0.0002393831,0.0000520318,0.0002280579,0.00002208255,0.000004476438,0.000009607025,0.00009101175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1289158,"threshold_uncertainty_score":0.4575905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05382848190465316,"score_gpt":0.3213678462117088,"score_spread":0.2675393643070557,"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."}}