{"id":"W1799642161","doi":"10.25336/p6cc87","title":"An Introduction to the Use of Linear Models with Correlated Data","year":2001,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Econometrics; Relevance (law); Computer science; Sampling (signal processing); Random effects model; Linear model; Population; Statistics; Generalized linear model; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002091884,0.0000592837,0.0001680147,0.0002504267,0.00008387075,0.00001456929,0.0001651917,0.00002911924,0.00002744082],"category_scores_gemma":[0.0001243059,0.00004975977,0.00001004577,0.000550443,0.00002370636,0.0004493392,0.00002535902,0.00005049774,0.00001373517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000108034,"about_ca_system_score_gemma":0.00001081743,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4225429,"about_ca_topic_score_gemma":0.8018438,"domain_scores_codex":[0.9993433,0.00001371931,0.0002642607,0.0002387308,0.00002266973,0.0001172938],"domain_scores_gemma":[0.9992234,0.00001519689,0.00009424112,0.0005727088,0.00003963368,0.00005479538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003099954,0.00001697252,0.5613334,0.000005676869,0.00009655266,0.000003658786,0.00117349,0.4149892,4.677639e-7,0.01347097,0.005211544,0.003667064],"study_design_scores_gemma":[0.000198909,0.00007034088,0.237774,0.00001439619,0.00002853013,0.000003727344,0.0004430698,0.6808994,6.187012e-7,0.001859396,0.0785186,0.000188936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985201,0.0006429225,0.008033098,0.004336701,0.0004598185,0.0002868822,0.000885801,0.00001178357,0.0001419807],"genre_scores_gemma":[0.9978074,0.0001492031,0.0007383618,0.0001734996,0.0001635795,0.000005476434,0.0008726264,0.000006702432,0.00008315111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3793009,"threshold_uncertainty_score":0.5813025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2749543061962489,"score_gpt":0.2980163105530558,"score_spread":0.02306200435680683,"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."}}