{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002220703,0.0001096738,0.0001880879,0.000101096,0.0001902049,0.0000455839,0.0001306866,0.00006057489,0.00000169143],"category_scores_gemma":[0.000001397099,0.0001074701,0.0001047314,0.0002540482,0.00004115934,0.00006950831,0.00003081877,0.0001748399,4.272671e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002345989,"about_ca_system_score_gemma":0.00001934878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.549661e-7,"about_ca_topic_score_gemma":0.00001669942,"domain_scores_codex":[0.9992211,0.00001694682,0.0003825705,0.000109213,0.00008090409,0.0001892703],"domain_scores_gemma":[0.9995847,0.000100093,0.0000794837,0.00009153254,0.00007565977,0.00006857872],"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":[0.00001535747,0.00004381612,0.0005991628,0.00006106082,0.0001039924,8.548792e-7,0.00002858738,0.716683,0.000001563707,0.002916956,0.1431812,0.1363644],"study_design_scores_gemma":[0.0004562705,0.00003960981,0.002535068,0.0000837815,0.00005148905,0.00001947219,0.000005223778,0.9211242,9.71268e-7,0.0018233,0.07377812,0.00008253575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002771979,0.0006927977,0.9950976,0.0002092844,0.0003935983,0.0003769154,0.000006169251,0.0003518651,0.00009978509],"genre_scores_gemma":[0.6071332,0.0001798747,0.3913134,0.0001570831,0.001061403,0.00009118069,0.00002720371,0.00001208924,0.00002457661],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6043612,"threshold_uncertainty_score":0.4382504,"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."}}