{"id":"W3204912691","doi":"10.21203/rs.3.rs-1743146/v1","title":"Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Texas Health Science Center at Houston; Patient-Centered Outcomes Research Institute; National Science Foundation; National Institutes of Health; Cancer Prevention and Research Institute of Texas","keywords":"Laplace's method; Generalized linear mixed model; Computer science; Algorithm; Gaussian; Computation; Laplace transform; Applied mathematics; Independence (probability theory); Regularization (linguistics); Mathematical optimization; Mathematics; Artificial intelligence; Statistics; Bayesian probability","routes":{"ca_aff":true,"ca_fund":true,"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","metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003730533,0.0004608193,0.0008824515,0.0002036363,0.001194736,0.000594264,0.00125516,0.0003932759,0.0006640278],"category_scores_gemma":[0.0227124,0.0004249225,0.0001827295,0.0003107639,0.0001559571,0.00008178227,0.00340079,0.002676502,0.00002761052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003552692,"about_ca_system_score_gemma":0.0004928017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005262275,"about_ca_topic_score_gemma":0.00006497681,"domain_scores_codex":[0.9923963,0.00285545,0.0006539674,0.001470231,0.00168399,0.000940048],"domain_scores_gemma":[0.9839898,0.0134358,0.0002613529,0.001338063,0.0006542852,0.0003206738],"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":[0.006413751,0.006631982,0.0009821155,0.02084765,0.003615437,0.0006375584,0.002758939,0.07330177,0.006632886,0.3833162,0.2648036,0.2300581],"study_design_scores_gemma":[0.0008558154,0.0005229018,0.0001840031,0.0004563412,0.00007675604,4.527908e-7,0.0002373565,0.6516025,0.0007252705,0.3437178,0.001246594,0.0003741793],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05726143,0.000277035,0.9138898,0.0003580503,0.0003417109,0.002705877,0.02461919,0.000262538,0.0002844172],"genre_scores_gemma":[0.2437457,0.0003544157,0.6925473,0.00006225026,0.0008259479,0.004679859,0.05654632,0.0002747617,0.0009634267],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5783007,"threshold_uncertainty_score":0.9998202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.392244178189707,"score_gpt":0.508235227543096,"score_spread":0.1159910493533891,"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."}}