{"id":"W4408396060","doi":"10.1016/j.jnca.2025.104166","title":"Hierarchical multi-scale spatio-temporal semantic graph convolutional network for traffic flow forecasting","year":2025,"lang":"en","type":"article","venue":"Journal of Network and Computer Applications","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Graph; Scale (ratio); Theoretical computer science; Cartography","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":[0.000331858,0.000738796,0.0006457384,0.001435715,0.0003636768,0.0005028196,0.00111272,0.0006902645,0.001455235],"category_scores_gemma":[0.0007363124,0.0003462619,0.0007887941,0.001440692,0.0002667977,0.001013988,0.0005668682,0.0008647318,0.000412672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009594657,"about_ca_system_score_gemma":0.00106531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04816403,"about_ca_topic_score_gemma":0.05453336,"domain_scores_codex":[0.999843,0.00001808694,0.00000774249,0.00006209728,0.00003008969,0.00003897395],"domain_scores_gemma":[0.9997824,0.00007276976,0.00002676606,0.00003181763,0.00006432857,0.00002188761],"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":[0.0004037258,0.0003495195,0.006024778,0.0001229519,0.0002198721,0.0002141456,0.00007289452,0.6649706,0.01116289,0.008401187,0.009856189,0.2982011],"study_design_scores_gemma":[0.000001566653,0.000006161879,0.0003957057,0.000002122425,0.000009907779,0.000005886832,0.000003589335,0.997863,0.0003376328,0.001189147,0.0001828373,0.000002380283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2115631,0.002446705,0.7728883,0.0007732933,0.0002790168,0.00006968931,0.003367728,0.004014688,0.00459748],"genre_scores_gemma":[0.9306629,0.0008229664,0.06031543,0.0001283533,0.0000813116,0.00004995141,0.003606274,0.00008309827,0.004249712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04816403,"threshold_uncertainty_score":0.09576738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01344112464202516,"score_gpt":0.2290410482172885,"score_spread":0.2155999235752633,"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."}}