{"id":"W2013623941","doi":"10.1002/cjs.5550360206","title":"Local influence in multilevel models","year":2008,"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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Multilevel model; Computation; Measure (data warehouse); Random effects model; Econometrics; Mathematics; Simple (philosophy); Statistics; Regression analysis; Matrix (chemical analysis); Regression; Computer science; Algorithm; Data mining","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":[0.0004828811,0.0001296922,0.0003665008,0.0002505667,0.0000837582,0.00002489743,0.0002487394,0.00007646358,0.0001811618],"category_scores_gemma":[0.003566299,0.0001158368,0.00003809842,0.0001539705,0.0003075654,0.0001546103,0.000008824766,0.0003592257,0.000009936156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001726652,"about_ca_system_score_gemma":0.00146525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002612155,"about_ca_topic_score_gemma":0.009084749,"domain_scores_codex":[0.9984242,0.0001172321,0.0007660781,0.0001085074,0.0002495461,0.0003343854],"domain_scores_gemma":[0.9973505,0.00118354,0.0002566834,0.0001554419,0.000472376,0.0005815186],"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":[0.00001329951,0.0000277914,0.004116077,0.00004131925,0.00001491957,0.003069818,0.001186868,0.0005200572,0.00001058759,0.9549562,0.00656634,0.02947671],"study_design_scores_gemma":[0.0003757271,0.00008451835,0.02076382,0.00009934975,0.00001321339,0.0004607753,0.00009483217,0.01467634,0.00002963458,0.9629617,0.0002914149,0.0001486468],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0214591,0.0000439434,0.9773024,0.0000559144,0.0001282768,0.00006227391,0.0002984666,0.000002666795,0.0006469559],"genre_scores_gemma":[0.4653268,0.00001193435,0.5345267,0.00007599607,0.00001957457,7.437612e-7,6.875763e-7,0.000009852166,0.00002766738],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4438677,"threshold_uncertainty_score":0.5069503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09810041417147447,"score_gpt":0.3278599594412723,"score_spread":0.2297595452697978,"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."}}