{"id":"W2896344278","doi":"10.1007/s00184-018-0690-z","title":"An approximate method for generalized linear and nonlinear mixed effects models with a mechanistic nonlinear covariate measurement error model","year":2018,"lang":"en","type":"article","venue":"Metrika","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"City University of New York; National Science Foundation","keywords":"Covariate; Mathematics; Estimator; Nonlinear system; Applied mathematics; Consistency (knowledge bases); Inference; Asymptotic distribution; Linear model; Statistics; Errors-in-variables models; Computer science; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003231368,0.0004364432,0.0009459048,0.0002192541,0.0002540374,0.0001040156,0.0002915582,0.0001799081,0.00001410425],"category_scores_gemma":[0.002918012,0.0003169203,0.00009433879,0.0004126335,0.0001257043,0.0001535852,0.00007349555,0.000185292,0.000003862629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007058181,"about_ca_system_score_gemma":0.0001200644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000362842,"about_ca_topic_score_gemma":0.000032312,"domain_scores_codex":[0.9970035,0.0004966253,0.0005493491,0.0007291095,0.0006449659,0.0005764395],"domain_scores_gemma":[0.996591,0.001371407,0.0002485486,0.0006434859,0.0008272599,0.0003183201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00122809,0.0006901784,0.000002894726,0.005321574,0.000359874,0.00001370803,0.000579899,0.000322922,0.01817033,0.9619175,0.00008038348,0.01131262],"study_design_scores_gemma":[0.001493569,0.0008146174,0.000001214473,0.000191727,0.0004374013,0.000006899633,0.00001385929,0.585282,0.01553417,0.3959396,0.0000119207,0.0002730421],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001561476,0.00006298822,0.9959326,0.00005458765,0.0001314613,0.001771718,0.0002133935,0.0001592603,0.0001125631],"genre_scores_gemma":[0.005150107,0.000009216254,0.9939245,0.0002066159,0.0002181768,0.0003193609,0.00001700259,0.0001048716,0.00005014376],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5849591,"threshold_uncertainty_score":0.9999283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1623960712519726,"score_gpt":0.4036202787245841,"score_spread":0.2412242074726114,"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."}}