{"id":"W1974232043","doi":"10.1002/cjs.10136","title":"A resampling approach to estimate variance components of multilevel models","year":2012,"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 Waterloo; Wilfrid Laurier University","funders":"","keywords":"Resampling; Estimator; Variance (accounting); Statistics; Computer science; Multilevel model; Econometrics; Cluster (spacecraft); Variance components; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001078803,0.0001522611,0.0004871095,0.0002035303,0.00008290761,0.00003561335,0.0002902091,0.00006620396,0.00005748613],"category_scores_gemma":[0.003560549,0.0001348602,0.00005042456,0.0001439283,0.0000932913,0.0001351115,0.00001896945,0.0002310229,0.000004947341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001032304,"about_ca_system_score_gemma":0.0003518525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000761428,"about_ca_topic_score_gemma":0.0001814308,"domain_scores_codex":[0.9981813,0.0001248808,0.0008350289,0.0001054725,0.0002996506,0.000453713],"domain_scores_gemma":[0.9966378,0.001113115,0.0004590409,0.0002149734,0.0004938904,0.001081198],"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":[0.00001756218,0.0000703763,0.0009032189,0.0001455618,0.00004205579,0.00001774407,0.001358043,0.0003103489,0.0001222972,0.9839635,0.002896819,0.01015247],"study_design_scores_gemma":[0.0003809496,0.0000952187,0.009952526,0.0002590101,0.00009774139,0.0001172522,0.0001099535,0.03872342,0.0000944053,0.9494401,0.0004935717,0.0002359096],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002892283,0.0000538708,0.9941409,0.00003022451,0.0003055628,0.0001273123,0.0009245662,0.000003247889,0.001522034],"genre_scores_gemma":[0.3137106,0.000001457677,0.6861349,0.00003891209,0.00006856334,0.000001656961,0.000003680904,0.00001780887,0.00002234414],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3108184,"threshold_uncertainty_score":0.5499439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.203346326797722,"score_gpt":0.3821604670666834,"score_spread":0.1788141402689615,"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."}}