{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001666243,0.0008375431,0.001144045,0.0005873638,0.000601647,0.001013809,0.002513714,0.001085036,0.0008952501],"category_scores_gemma":[0.002654614,0.0003612767,0.0006245366,0.0008237812,0.0006037471,0.00214422,0.001543049,0.001538015,0.0003031153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001264219,"about_ca_system_score_gemma":0.001464449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01359175,"about_ca_topic_score_gemma":0.01320549,"domain_scores_codex":[0.9992512,0.0001296988,0.00003807671,0.0002955252,0.0001714373,0.0001140499],"domain_scores_gemma":[0.9990059,0.000258937,0.0001021536,0.0002333324,0.0003050963,0.00009460777],"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":[0.0002119517,0.0002953846,0.003700562,0.00002667406,0.00006959442,0.0001044133,0.0001002195,0.8497023,0.003065209,0.004076259,0.002318022,0.1363294],"study_design_scores_gemma":[0.000002791854,0.00001071486,0.00009888015,9.268666e-7,0.000003463172,0.000006239045,0.000004386318,0.9981369,0.0003606867,0.001273126,0.00009927207,0.00000262337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04580416,0.0001946498,0.949447,0.0002519653,0.00004666569,0.00004899794,0.0001362385,0.003022949,0.001047362],"genre_scores_gemma":[0.8736277,0.0001026691,0.1236283,0.0002041084,0.00004843623,0.00008661165,0.0003827905,0.00007511851,0.001844256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01359175,"threshold_uncertainty_score":0.02702522,"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."}}