{"id":"W4413420566","doi":"10.1016/j.inffus.2025.103635","title":"Error adjustment based on spatiotemporal correlation fusion for traffic forecasting","year":2025,"lang":"en","type":"article","venue":"Information Fusion","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Correlation; Fusion; Artificial intelligence; Data mining; Machine learning; Mathematics","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":[0.0001833446,0.0001211884,0.00009092446,0.0003924478,0.0001221136,0.00003878109,0.00007167935,0.0001047661,0.00002298334],"category_scores_gemma":[0.00003499576,0.0001208055,0.00005071997,0.0001977718,0.000008225653,0.0005643389,0.00001543793,0.00009258777,0.00002314499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001295495,"about_ca_system_score_gemma":0.00001663512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002712337,"about_ca_topic_score_gemma":0.000004155063,"domain_scores_codex":[0.9992893,0.000009972106,0.0003341249,0.00007641358,0.0001577667,0.0001324496],"domain_scores_gemma":[0.9996713,0.00004645047,0.00006540531,0.0001324923,0.00005469115,0.00002960434],"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":[0.00004726899,0.00001882957,0.00001186824,0.0001406052,0.000005541346,7.60887e-8,0.0001115013,0.5167574,0.00004541465,0.00121268,0.07827434,0.4033745],"study_design_scores_gemma":[0.0006734223,0.00007207575,0.0008128642,0.0001200451,0.00001234075,1.678197e-7,0.00006496675,0.8982126,0.0003526648,0.00002175273,0.09956136,0.0000958099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01932705,0.00001485662,0.9518674,0.000297375,0.001451079,0.00116458,0.00002657543,0.003833666,0.02201739],"genre_scores_gemma":[0.9939618,0.00001479253,0.00457589,0.0005902897,0.0000440126,0.0001498549,0.0005696511,0.000008893279,0.00008484249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9746347,"threshold_uncertainty_score":0.4926305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01355195137682578,"score_gpt":0.2245076782904326,"score_spread":0.2109557269136068,"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."}}