{"id":"W2182010196","doi":"","title":"NEIGHBOURHOOD FACTORS AND CHILDREN: SMALL AREA STATISTICS","year":2003,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Small area estimation; Estimator; Statistics; Mean squared error; Mathematics; Neighbourhood (mathematics); Metropolitan area; Econometrics; Geography","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":[0.02876834,0.0003794484,0.00146562,0.001653397,0.000611361,0.001379678,0.001548134,0.0009862422,0.003425812],"category_scores_gemma":[0.1593209,0.0002909903,0.0009273607,0.00264565,0.002733255,0.002589986,0.001578758,0.001493625,0.0002048545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000694739,"about_ca_system_score_gemma":0.0009146106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007493102,"about_ca_topic_score_gemma":0.007864684,"domain_scores_codex":[0.9797672,0.01510842,0.0005653774,0.001914998,0.00242533,0.0002186476],"domain_scores_gemma":[0.7105975,0.2587641,0.01493047,0.01191962,0.002719952,0.001068367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001297568,0.0001953715,0.7423575,0.0005543972,0.003613306,0.0005854103,0.001648328,0.04223017,0.0004942471,0.06377679,0.002600225,0.1406467],"study_design_scores_gemma":[0.0002609433,0.002029563,0.622304,0.0003332867,0.001474032,0.001342103,0.002023821,0.1894907,0.001317649,0.1639582,0.01533357,0.0001322667],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6442812,0.006041443,0.3394703,0.001336891,0.0002755774,0.0002239797,0.0006682986,0.0001719224,0.007530405],"genre_scores_gemma":[0.9695075,0.0006029014,0.02786019,0.0001733851,0.0001325849,0.0001367326,0.0005069969,0.00004307729,0.001036679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02876834,"threshold_uncertainty_score":0.1521434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06074930620913539,"score_gpt":0.3190212886509765,"score_spread":0.2582719824418411,"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."}}