{"id":"W4408958106","doi":"10.1016/j.engappai.2025.110570","title":"Graph convolutional network for traffic incidents duration classification","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Hong Kong Institute for Data Science; Hong Kong Government; Research Grants Council, University Grants Committee; City University of Hong Kong","keywords":"Computer science; Graph; Convolutional neural network; Duration (music); Artificial intelligence; Theoretical computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001803696,0.0001074472,0.0001128551,0.0002256903,0.00007316121,0.00002203956,0.0001869663,0.00007331936,0.000004574615],"category_scores_gemma":[0.00002904713,0.0001333356,0.0000620723,0.0005865697,0.00003596468,0.0000873802,0.00001382239,0.00008047009,0.000007470422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005727863,"about_ca_system_score_gemma":0.00001537405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000218608,"about_ca_topic_score_gemma":0.000006495055,"domain_scores_codex":[0.9991629,0.000004872545,0.0004361672,0.0001540356,0.000092259,0.0001497469],"domain_scores_gemma":[0.9995347,0.00008160104,0.00004633485,0.0002115113,0.00009721635,0.00002864332],"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":[0.000004907066,0.00002844083,0.000008594075,0.00008189584,0.00003044682,2.100667e-8,0.00002623901,0.6317453,0.003226994,0.2966625,0.003645408,0.06453931],"study_design_scores_gemma":[0.00002251959,0.00001204121,0.0004703416,0.0000377236,0.00002365712,2.587668e-7,0.00004857593,0.9749089,0.0087745,0.004370774,0.0112192,0.000111539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003851289,0.0001650285,0.992838,0.00008154461,0.0002821496,0.0007080844,0.0000180471,0.001730317,0.0003255321],"genre_scores_gemma":[0.967186,0.00008350353,0.03152756,0.00001322434,0.00008865904,0.0009967295,0.00006488603,0.00001405699,0.00002532336],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9633347,"threshold_uncertainty_score":0.5437269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01753527209912228,"score_gpt":0.2630053365861533,"score_spread":0.245470064487031,"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."}}