{"id":"W44373347","doi":"","title":"BENCHMARKING HIERARCHICAL BAYES SMALL AREA ESTIMATORS WITH APPLICATION IN CENSUS UNDERCOVERAGE ESTIMATION","year":2002,"lang":"en","type":"article","venue":"","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Small area estimation; Estimator; Statistics; Bayes' theorem; Gibbs sampling; Econometrics; Bayes estimator; Benchmark (surveying); Sampling (signal processing); Mean squared error; Posterior probability; Mathematics; Estimation; Census; Bayesian probability; Computer science; Geography; Population; Economics; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"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":[0.001144393,0.0001602186,0.0002154468,0.0004667633,0.0001386196,0.0002307535,0.0002922028,0.00009334492,0.0002247261],"category_scores_gemma":[0.0003446495,0.000112572,0.00005370146,0.001204564,0.00007190386,0.0002998312,0.00003613608,0.0001768799,0.00008638229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004445438,"about_ca_system_score_gemma":0.00001595178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001408353,"about_ca_topic_score_gemma":0.0006166091,"domain_scores_codex":[0.9976215,0.0001051898,0.0005870566,0.0005582122,0.0008594356,0.0002686092],"domain_scores_gemma":[0.9983763,0.0007963874,0.0001706273,0.0004266142,0.0001282261,0.0001018564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005791997,0.0002365626,0.06744549,0.00001141234,0.0000121169,0.00001383746,0.0008359935,0.3046623,0.00009552995,0.02063078,0.0006416408,0.6053563],"study_design_scores_gemma":[0.0003442993,0.00005890138,0.01813206,0.00002486279,0.000006449328,0.000011421,0.0002003924,0.9482076,0.00001634638,0.03257143,0.0002712875,0.0001549845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4011963,0.00002845166,0.5888271,0.0005470861,0.00004467011,0.0001658687,0.000003352197,0.00007033819,0.009116796],"genre_scores_gemma":[0.9517158,0.00002051732,0.04782739,0.0001574354,0.00001990475,0.00003381284,0.00002540907,0.00001192889,0.0001877841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6435452,"threshold_uncertainty_score":0.4590555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1079905793921092,"score_gpt":0.3187521053151492,"score_spread":0.2107615259230401,"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."}}