{"id":"W1979475093","doi":"10.1093/biomet/92.1.183","title":"On measuring the variability of small area estimators under a basic area level model","year":2005,"lang":"en","type":"article","venue":"Biometrika","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":177,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Mathematics; Estimator; Small area estimation; Mean squared error; Statistics; Residual; Bias of an estimator; Variance (accounting); Minimum-variance unbiased estimator; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02215589,0.0006317728,0.001237865,0.001635105,0.0004395336,0.001286855,0.001667907,0.001527562,0.0004133246],"category_scores_gemma":[0.1397343,0.0004791296,0.0007445755,0.001963336,0.002326162,0.003328051,0.00146989,0.001522374,0.0001404916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009177178,"about_ca_system_score_gemma":0.0007402301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002301855,"about_ca_topic_score_gemma":0.001619493,"domain_scores_codex":[0.9902624,0.005719538,0.0003144892,0.001768775,0.001764252,0.0001704983],"domain_scores_gemma":[0.8401673,0.1391343,0.007454417,0.009586349,0.003307052,0.0003505904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001182434,0.00005986938,0.02207675,0.0001017733,0.0002864709,0.00009595005,0.0002799805,0.8945901,0.004263779,0.03771999,0.0002741868,0.04013291],"study_design_scores_gemma":[0.000009178893,0.0000741987,0.007177671,0.00001862906,0.00002083973,0.0001199798,0.00002637416,0.9599344,0.001949535,0.03033021,0.0003001384,0.00003889578],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07700806,0.0001815539,0.9220063,0.00008088537,0.00001272604,0.00002078987,0.00004496431,0.000145573,0.000499215],"genre_scores_gemma":[0.7672119,0.0002112459,0.2317674,0.0000781413,0.0000617789,0.00008024667,0.0002393366,0.00009707107,0.0002530047],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02215589,"threshold_uncertainty_score":0.117173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.391344917065959,"score_gpt":0.3662041838385975,"score_spread":0.02514073322736154,"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."}}