{"id":"W184316663","doi":"10.1007/bf03404950","title":"An Introduction to Multilevel Regression Models","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":147,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Institute for Clinical Evaluative Sciences; University of Toronto","funders":"Health Canada","keywords":"Multilevel model; Hierarchical database model; Computer science; Regression analysis; Regression; Statistical model; Data mining; Statistics; Artificial intelligence; Machine learning; Econometrics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002820237,0.00009921447,0.0002746495,0.0003955546,0.000166106,0.00009588767,0.0002283815,0.00005846876,0.0003413585],"category_scores_gemma":[0.003149349,0.00007837291,0.00003750021,0.0002633037,0.00003770699,0.000353277,0.000005403239,0.0002468624,0.000005312856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004231175,"about_ca_system_score_gemma":0.003068265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002494993,"about_ca_topic_score_gemma":0.008998668,"domain_scores_codex":[0.9982578,0.0003746898,0.0005108726,0.0001430298,0.0002049966,0.0005086423],"domain_scores_gemma":[0.9956919,0.0001689131,0.0002432697,0.0002396962,0.0003982517,0.003257962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004976488,0.00003359119,0.0004195571,0.00001982759,0.000006940704,0.00003388261,0.001243575,0.000009105033,0.00001687749,0.4106328,0.02807324,0.5595056],"study_design_scores_gemma":[0.0003950365,0.0007598405,0.004792122,0.0001220819,0.000007960845,0.0005319281,0.0009050375,0.005182523,0.00001430792,0.8976702,0.08939784,0.0002211366],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01704072,0.00004446709,0.9358322,0.04620619,0.0004802161,0.0001036213,0.00001751622,0.000008705069,0.0002663551],"genre_scores_gemma":[0.3073399,0.00001586638,0.6903162,0.001222734,0.0009972296,0.000001922746,0.000001886194,0.00001719683,0.00008708625],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5592845,"threshold_uncertainty_score":0.544297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2217328318533305,"score_gpt":0.4339817907005796,"score_spread":0.2122489588472491,"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."}}