{"id":"W2546295387","doi":"10.1002/cjs.11303","title":"Hierarchical Bayesian small area estimation for circular data","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Small area estimation; Bayesian probability; Estimation; Bayes estimator; Bayesian hierarchical modeling; Computer science; Statistics; Mathematics; Bayesian inference; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000335468,0.00008129085,0.0001225437,0.0000618605,0.0001167494,0.00004202781,0.0003728785,0.00003747526,0.0004714374],"category_scores_gemma":[0.001167095,0.00006225214,0.0000201399,0.00006263742,0.0001525326,0.0001154899,0.00003183518,0.0000707919,0.0000225093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001708628,"about_ca_system_score_gemma":0.0002912087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001351215,"about_ca_topic_score_gemma":0.01497822,"domain_scores_codex":[0.9991674,0.00002148221,0.0002781653,0.0001360757,0.0001372323,0.0002596257],"domain_scores_gemma":[0.9987697,0.0002814136,0.0001532684,0.0002354261,0.00004195084,0.000518269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002591836,0.00002568417,0.0203415,0.00004439534,0.00006731514,0.0004094104,0.0004424794,0.001109526,0.0007038496,0.01930279,0.1494514,0.8080757],"study_design_scores_gemma":[0.002358101,0.0005204568,0.08769769,0.0002915425,0.0002293322,0.0004500463,0.0001150363,0.1676076,0.00009610922,0.2987508,0.4411389,0.0007443785],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003268712,0.00002426047,0.9932371,0.0007496337,0.0002475287,0.00008906973,0.002088744,0.00000243417,0.0002924568],"genre_scores_gemma":[0.5430876,0.00001559173,0.4563228,0.0002372853,0.00008831628,0.000001956094,0.00007831251,0.00001789688,0.0001503098],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8073313,"threshold_uncertainty_score":0.83582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05310494140133249,"score_gpt":0.2381798791264867,"score_spread":0.1850749377251542,"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."}}