{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.05163281,0.0002738963,0.0006945209,0.0004705333,0.00007267875,0.00004379086,0.0003128319,0.0003860501,0.0004003736],"category_scores_gemma":[0.02317187,0.0002623035,0.00007852363,0.0007110535,0.00007503028,0.0001808554,0.00009730448,0.0004024202,0.00009147714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007611829,"about_ca_system_score_gemma":0.0001558342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006372406,"about_ca_topic_score_gemma":0.008610399,"domain_scores_codex":[0.969604,0.0283122,0.0007878898,0.0005516409,0.0002494843,0.0004948271],"domain_scores_gemma":[0.9324145,0.06639218,0.000247175,0.0006218445,0.0002411807,0.00008308396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00206513,0.0002549383,0.9745179,0.0001077901,0.00009171698,0.000005885116,0.0009265218,0.003226171,0.003179071,0.01341622,0.0006580393,0.001550586],"study_design_scores_gemma":[0.000530959,0.0001052338,0.9351648,0.00003985982,0.000008472342,0.000009781493,0.00007832022,0.003209205,0.002861825,0.05753006,0.0001118431,0.0003496242],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5688576,0.00002675556,0.4301544,0.00004085719,0.0003053961,0.0002509675,0.00005108533,0.0001612406,0.0001516626],"genre_scores_gemma":[0.5342919,0.00001258195,0.4643499,0.0001347264,0.00003145093,0.00002894699,0.0003466789,0.00006032348,0.0007435362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04411384,"threshold_uncertainty_score":0.9999829,"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."}}