{"id":"W2009687799","doi":"10.1002/cjs.11220","title":"Bayesian sensitivity analyses for hidden sub‐populations in weighted sampling","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Medical Expenditure Panel Survey; Statistics; Bayesian probability; Econometrics; Population; Sampling (signal processing); Sample (material); Sensitivity (control systems); Health care; Computer science; Mathematics; Medicine; Environmental health; Economics; Health insurance","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001095881,0.0001416383,0.0004281092,0.0003629352,0.0001340827,0.0000717365,0.0001107637,0.00008036469,0.00006262103],"category_scores_gemma":[0.008320788,0.0001320401,0.00006729531,0.0002239011,0.00008919822,0.00007595944,0.000006127926,0.0002199289,0.000001787219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001419708,"about_ca_system_score_gemma":0.0005020175,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001409287,"about_ca_topic_score_gemma":0.062897,"domain_scores_codex":[0.9984679,0.0002395422,0.0006614233,0.0001286944,0.0001564694,0.0003459555],"domain_scores_gemma":[0.9953914,0.003251451,0.0003231985,0.0001512132,0.0004099275,0.0004728361],"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":[0.00001432752,0.00002165564,0.004955566,0.00008390617,0.00003122928,0.0001026324,0.000220216,0.00001755286,0.0002023295,0.9242541,0.002967945,0.06712853],"study_design_scores_gemma":[0.0003540858,0.00009619797,0.01249171,0.0001191279,0.00009623429,0.00005780178,0.00006695752,0.02193331,0.0001270155,0.9639911,0.000499933,0.0001665522],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006733711,0.000021336,0.9917847,0.0001806456,0.0003097879,0.0001267615,0.0006494492,0.000004948773,0.0001886965],"genre_scores_gemma":[0.3439986,0.000002043882,0.6557989,0.00005108577,0.0001123618,0.000001774086,0.000009596087,0.00001562691,0.00001007028],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3372648,"threshold_uncertainty_score":0.9961362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2100766677044757,"score_gpt":0.41858946097469,"score_spread":0.2085127932702143,"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."}}