{"id":"W1986211349","doi":"10.1016/j.csda.2012.03.011","title":"Bootstrap variance estimation with survey data when estimating model parameters","year":2012,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Sampling design; Statistics; Sampling (signal processing); Variance (accounting); Simple random sample; Stratified sampling; Mathematics; Inference; Population; Econometrics; Computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002542459,0.0003049062,0.000597949,0.0001963619,0.0002533793,0.0002383731,0.001141013,0.00007256152,0.0001648835],"category_scores_gemma":[0.006174902,0.0002690505,0.00003396285,0.0007690269,0.0001756075,0.0008931258,0.0005422796,0.0002228865,0.00002564036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005669867,"about_ca_system_score_gemma":0.0001812407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006113427,"about_ca_topic_score_gemma":0.0003138038,"domain_scores_codex":[0.996792,0.0004315529,0.000734106,0.0007545647,0.0008217376,0.0004661094],"domain_scores_gemma":[0.9895571,0.007470761,0.0004802536,0.001899138,0.0003362146,0.0002565664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006915961,0.000421966,0.02111594,0.000171191,0.002311647,0.00001106806,0.000396649,0.391901,9.16406e-7,0.502951,0.01722568,0.06342374],"study_design_scores_gemma":[0.0001345688,0.00001393728,0.0132452,0.00001702306,0.001299408,0.000003404786,0.000006175913,0.5903981,2.830424e-7,0.3946642,0.000006890157,0.0002107419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002293907,0.00002710271,0.9609818,0.00004533671,0.00007125096,0.0001836945,0.03833071,0.00006395206,0.00006674187],"genre_scores_gemma":[0.04147711,0.000002514605,0.9166991,0.00007011418,0.00004875478,0.00001045351,0.04163787,0.0000328426,0.00002123975],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1984971,"threshold_uncertainty_score":0.9999762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3189255223302025,"score_gpt":0.446430431089935,"score_spread":0.1275049087597325,"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."}}