{"id":"W4414511714","doi":"10.1111/exsy.70141","title":"Traversal Learning Coordination for Lossless and Efficient Distributed Learning","year":2025,"lang":"en","type":"article","venue":"Expert Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea; National Research Foundation","keywords":"Tree traversal; Lossless compression; Node (physics); Independent and identically distributed random variables; Federated learning; Distributed learning","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":[0.004678679,0.0008005716,0.001218922,0.0006044504,0.0007246507,0.001799075,0.003186681,0.001185112,0.002564964],"category_scores_gemma":[0.01266392,0.0005059015,0.0005075485,0.001020296,0.00158811,0.003385096,0.003672978,0.002296916,0.0007849853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001720071,"about_ca_system_score_gemma":0.002842172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004180397,"about_ca_topic_score_gemma":0.004593404,"domain_scores_codex":[0.9975775,0.0008629299,0.0001303704,0.000648889,0.000525479,0.0002548895],"domain_scores_gemma":[0.994454,0.002430224,0.0003988156,0.001581664,0.0008248271,0.0003104769],"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":[0.0004353672,0.0002764456,0.002133673,0.0001040987,0.00007498851,0.0001270288,0.0001939719,0.7304619,0.004164473,0.03769047,0.005463426,0.2188742],"study_design_scores_gemma":[0.00001783248,0.00003844516,0.00005922223,0.000002989568,0.000003550268,0.00001322415,0.00001287629,0.9873372,0.0008142696,0.01114686,0.000550175,0.000003329346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01056036,0.0001066212,0.98712,0.0001941395,0.00002534471,0.00003412108,0.00003635139,0.001254463,0.0006685936],"genre_scores_gemma":[0.7295597,0.0001080442,0.2665237,0.0002700256,0.0000699841,0.0001877142,0.0002918362,0.0002563881,0.002732709],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004678679,"threshold_uncertainty_score":0.02474356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01203741173671372,"score_gpt":0.2664987142009598,"score_spread":0.2544613024642461,"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."}}