{"id":"W4379116774","doi":"10.1109/tits.2023.3279929","title":"GraphSAGE-Based Dynamic Spatial–Temporal Graph Convolutional Network for Traffic Prediction","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Graph; Convolutional neural network; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003529024,0.0003333692,0.0002981425,0.0007121054,0.0002685392,0.00005983594,0.0001649122,0.0002120884,0.00003093782],"category_scores_gemma":[0.000001217121,0.0003750786,0.0003455588,0.0009396266,0.00006354101,0.0001732045,1.463952e-7,0.0002340149,0.0000611107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001374383,"about_ca_system_score_gemma":0.00003455913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005742381,"about_ca_topic_score_gemma":0.0005305422,"domain_scores_codex":[0.9979182,0.00004728759,0.0007867131,0.0003935545,0.0004178608,0.0004363124],"domain_scores_gemma":[0.9992837,0.0001195938,0.00008577423,0.0002592217,0.0001146782,0.0001369861],"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":[0.00008441731,0.00007453663,0.00004981985,0.0002764035,0.0001654091,0.000003301058,0.000135205,0.980953,0.0001632026,0.0003090517,0.01038895,0.007396725],"study_design_scores_gemma":[0.0007295215,0.0001945713,0.001119005,0.0001807598,0.0001370829,0.000001595744,0.0002444583,0.9864598,0.001034653,0.00005119565,0.009504667,0.0003426866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01153726,0.0001041163,0.9677135,0.00005009431,0.005445947,0.001680607,0.001357003,0.01203286,0.00007860317],"genre_scores_gemma":[0.995684,0.0003141758,0.0005688798,0.00003747136,0.0001085282,0.001747541,0.001248112,0.0000918849,0.000199415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9841467,"threshold_uncertainty_score":0.9998701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655555051913524,"score_gpt":0.2314106479643883,"score_spread":0.2148550974452531,"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."}}