{"id":"W2100426639","doi":"10.1111/insr.12097","title":"Calibration Weighting Methods for Complex Surveys","year":2015,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weighting; Trimming; Calibration; Range (aeronautics); Focus (optics); A-weighting; Statistics; Mathematics; Computer science; Algorithm; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004444214,0.0001591111,0.0004297506,0.00003094485,0.00005245957,0.0000374178,0.0002197034,0.00004426788,0.0004523396],"category_scores_gemma":[0.03394039,0.0001294875,0.00007665381,0.00008198087,0.00006334045,0.0001205244,0.00006446608,0.0001104286,0.00001626882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009383039,"about_ca_system_score_gemma":0.00006067365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009894501,"about_ca_topic_score_gemma":0.000003898572,"domain_scores_codex":[0.997646,0.0008809126,0.0006389068,0.0002946567,0.0003175317,0.0002220301],"domain_scores_gemma":[0.9912446,0.007718551,0.0001758603,0.0001674239,0.0004681997,0.0002253295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000008090514,0.00003633333,0.000004351212,0.000297576,0.00002210817,0.000001831258,0.000009468988,0.000001984499,0.0000351155,0.6662243,0.0134166,0.3199422],"study_design_scores_gemma":[0.0002782115,0.00005252836,0.0000284192,0.0002931387,0.00005123483,0.000007809261,0.000004693424,0.05660539,0.00002701186,0.7998151,0.1426957,0.0001407539],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001492417,0.001356159,0.9917532,0.001393311,0.000365529,0.0005587548,0.0004414064,0.00005797816,0.004072225],"genre_scores_gemma":[0.0003290325,0.0005675078,0.9972602,0.0009135925,0.0001520116,0.0001548843,0.0002459202,0.00002832674,0.000348594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3198015,"threshold_uncertainty_score":0.9741971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5384029956858,"score_gpt":0.6170284295987941,"score_spread":0.07862543391299415,"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."}}