{"id":"W4402192705","doi":"10.1002/cjs.11831","title":"Distributed learning for kernel mode–based regression","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Control Systems and Identification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Kernel (algebra); Mode (computer interface); Kernel regression; Regression; Artificial intelligence; Machine learning; Statistics; Mathematics; Human–computer interaction","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001285705,0.00005907087,0.0001008278,0.0001295826,0.00005622633,0.0001219659,0.00005593309,0.0000347132,0.00002644828],"category_scores_gemma":[0.0001297515,0.00005371276,0.00003707558,0.00007273758,0.00001038719,0.00006169068,7.186242e-7,0.0001277809,0.000005498834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001614728,"about_ca_system_score_gemma":0.0002606648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003119189,"about_ca_topic_score_gemma":0.002294906,"domain_scores_codex":[0.9995289,0.0000102937,0.0002226212,0.00004426711,0.00006627543,0.0001275873],"domain_scores_gemma":[0.9995128,0.00008589309,0.0000385958,0.00004526558,0.0001287708,0.000188701],"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":[0.00001743927,0.000005300244,0.000692212,0.0009181188,0.0001615013,0.0003036282,0.0005644217,0.4859483,0.005105543,0.009974075,0.400822,0.09548751],"study_design_scores_gemma":[0.0001659386,0.0000281735,0.0008520884,0.000233151,0.0000321441,0.00001326986,0.00004204617,0.8721826,0.00008301761,0.0007604432,0.1255366,0.00007050964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004306233,0.001406665,0.9922507,0.0001018705,0.001099911,0.00006530508,0.0006871733,0.00001957132,0.00006260023],"genre_scores_gemma":[0.9970619,0.000011225,0.002496185,0.000005212472,0.0001363538,0.000002654233,0.00008469929,0.00001925898,0.0001825032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9927557,"threshold_uncertainty_score":0.2190343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01202203004559688,"score_gpt":0.2254015715348227,"score_spread":0.2133795414892258,"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."}}