{"id":"W6921177708","doi":"10.6084/m9.figshare.c.6250631.v1","title":"Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources","year":2022,"lang":"en","type":"other","venue":"Figshare","topic":"Religion, Theology, History, Judaism, Christianity","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Generalized linear mixed model; Computation; Singularity; Regularization (linguistics); Gaussian; Noisy data; Mixed model; Scheme (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009530345,0.001690334,0.00201631,0.00186914,0.0009153809,0.001758624,0.003116313,0.002114471,0.00458839],"category_scores_gemma":[0.02541934,0.001039842,0.00335182,0.002128403,0.001143822,0.002450962,0.003359916,0.002937877,0.001698704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001551513,"about_ca_system_score_gemma":0.002898825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006425304,"about_ca_topic_score_gemma":0.00906467,"domain_scores_codex":[0.9950615,0.003006889,0.0002819035,0.00101097,0.0004525767,0.000186216],"domain_scores_gemma":[0.991308,0.005653788,0.0005922322,0.001216007,0.001042744,0.0001871248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003416363,0.000179639,0.004422804,0.0003503098,0.0007005919,0.0002725155,0.0004661552,0.5163898,0.001923228,0.04102823,0.005556512,0.4283686],"study_design_scores_gemma":[0.00003915808,0.00003981716,0.000375205,0.00003430716,0.00004918067,0.00005874659,0.00004482155,0.9518452,0.0007664416,0.04499683,0.001731062,0.00001909785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00190621,0.0001167582,0.9969758,0.0001225508,0.00002243594,0.00003852821,0.00009127742,0.0005892327,0.000137175],"genre_scores_gemma":[0.06523485,0.0001956334,0.9315112,0.0002609908,0.00006518095,0.000473128,0.0007136761,0.0002395296,0.00130574],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009530345,"threshold_uncertainty_score":0.05040193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08517396833821196,"score_gpt":0.3234688179067559,"score_spread":0.2382948495685439,"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."}}