{"id":"W7117470194","doi":"10.1016/j.mlwa.2025.100829","title":"A traffic-aware federated learning prediction framework with custom aggregation","year":2025,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Wilfrid Laurier University","keywords":"Adaptability; Data aggregator; Personalization; Generalization; Intelligent transportation system; Independent and identically distributed random variables; Raw data; Traffic flow (computer networking)","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":[],"consensus_categories":[],"category_scores_codex":[0.0001197341,0.0002117467,0.000159099,0.0002590461,0.0004778852,0.0001213372,0.0001234475,0.0001165719,0.00002261806],"category_scores_gemma":[0.00001692088,0.0001876327,0.00002960068,0.0009075183,0.00004668754,0.0001379425,0.00002149619,0.0007891579,0.00002271854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009425791,"about_ca_system_score_gemma":0.00002620629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001430987,"about_ca_topic_score_gemma":0.00004442786,"domain_scores_codex":[0.9990591,0.00004317951,0.0002073841,0.0002946625,0.0001743309,0.000221378],"domain_scores_gemma":[0.9995407,0.00006047946,0.00006106569,0.0002001046,0.00007668407,0.00006097292],"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":[0.00003738471,0.00004848008,0.005864265,0.0001005096,0.000122728,0.000001902812,0.0001528295,0.8793947,0.00007199222,0.002923084,0.001505138,0.109777],"study_design_scores_gemma":[0.0005020989,0.0001215371,0.003372794,0.0002175507,0.00007903579,0.00000842705,0.0001942827,0.8313629,0.0001686449,0.0000471877,0.1637,0.000225557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01240919,0.0001422506,0.964881,0.0002772352,0.00004172631,0.0007223381,0.00000683717,0.01390323,0.007616207],"genre_scores_gemma":[0.9905907,0.0001065836,0.006957458,0.00006474384,0.00005086748,0.00101347,0.0002383718,0.00005037112,0.0009274405],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9781815,"threshold_uncertainty_score":0.7651439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003282495210607881,"score_gpt":0.2073670888002791,"score_spread":0.2040845935896712,"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."}}