{"id":"W3041227018","doi":"","title":"“Optimal” calibration weights under unit nonresponse in survey sampling","year":2019,"lang":"en","type":"article","venue":"Survey methodology","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistics; Estimator; Calibration; Sampling (signal processing); Sample (material); Variance (accounting); Population; Survey sampling; Econometrics; Non-response bias; Mathematics; Computer science; Demography; Physics; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06388973,0.001033686,0.00243286,0.002101166,0.001384852,0.002221363,0.003782197,0.003339958,0.005012396],"category_scores_gemma":[0.2629014,0.001599812,0.0009371731,0.004123416,0.003975992,0.006147632,0.005896393,0.004531858,0.001915366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001627803,"about_ca_system_score_gemma":0.001831687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001076811,"about_ca_topic_score_gemma":0.0007737122,"domain_scores_codex":[0.9243609,0.06207899,0.001898305,0.005024337,0.005299482,0.001338106],"domain_scores_gemma":[0.8827135,0.08107825,0.006095565,0.02367013,0.005558519,0.0008840414],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003500034,0.0001763569,0.006284421,0.0006780103,0.0002304253,0.00008838854,0.0009394901,0.0391699,0.001056733,0.6701987,0.006650192,0.2741775],"study_design_scores_gemma":[0.0001396617,0.0001463442,0.003138608,0.0002128869,0.00007811106,0.0001852616,0.0002265832,0.1000987,0.001681118,0.8878562,0.006185129,0.00005125244],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01988268,0.0006377839,0.9730607,0.001721151,0.0001515377,0.0001810867,0.0001380073,0.000255798,0.003971313],"genre_scores_gemma":[0.4094324,0.001673479,0.5801427,0.001599985,0.0006723944,0.001535684,0.0005625566,0.0002854453,0.004095412],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9361103,"threshold_uncertainty_score":0.3378852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6246140539733327,"score_gpt":0.4963449919555593,"score_spread":0.1282690620177734,"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."}}