{"id":"W2168126634","doi":"10.1093/biostatistics/kxs028","title":"Testing multiple variance components in linear mixed-effects models","year":2012,"lang":"en","type":"article","venue":"Biostatistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Mathematics; Statistics; Test statistic; Null distribution; Estimator; Variance (accounting); Statistical hypothesis testing; Variance-based sensitivity analysis; One-way analysis of variance; Context (archaeology); Analysis of variance","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0005312062,0.0002083698,0.0003383442,0.00007372894,0.0000757434,0.00002493809,0.000160711,0.0001030407,0.0000189912],"category_scores_gemma":[0.01049407,0.0001942176,0.00002655234,0.0002678356,0.0000753455,0.0001327622,0.00008257385,0.0002148668,0.00004719768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005978022,"about_ca_system_score_gemma":0.00002534824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008504954,"about_ca_topic_score_gemma":0.000008914762,"domain_scores_codex":[0.9983423,0.000226049,0.0004207176,0.0002198458,0.0002436612,0.0005474719],"domain_scores_gemma":[0.9902945,0.009022687,0.0001368392,0.0002662193,0.00009222258,0.0001875591],"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":[0.00001989065,0.0004220424,0.0273788,0.0003736289,0.00001560142,0.00003789537,0.0003301798,0.00001312416,0.001867949,0.9342376,0.0008470415,0.03445625],"study_design_scores_gemma":[0.0006478045,0.00006324928,0.07043312,0.0002251893,0.00003383165,0.00001195125,0.00002932106,0.1716207,0.0006343875,0.7558316,0.0001303018,0.0003385839],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01287924,0.00006442446,0.9848731,0.00001778725,0.0006567786,0.0003011605,0.0001862077,0.00007434246,0.0009470058],"genre_scores_gemma":[0.4166419,0.00000269168,0.5831574,0.00004489686,0.00009047231,0.00001618669,0.000008368973,0.00002126139,0.00001688979],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4037626,"threshold_uncertainty_score":0.9978409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.178299709455031,"score_gpt":0.3677664905725939,"score_spread":0.1894667811175629,"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."}}