{"id":"W2968911474","doi":"10.1109/tits.2019.2932785","title":"DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":110,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Traffic flow (computer networking); Context (archaeology); Computer science; Data mining; Deep learning; Flow (mathematics); Interval (graph theory); Artificial intelligence; Machine learning; Geography; Mathematics","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":[0.0001633616,0.0002847412,0.0003830737,0.0003402926,0.00009277072,0.00003471075,0.0001483462,0.0001600573,0.00005082805],"category_scores_gemma":[0.00000301267,0.0002897054,0.0002522673,0.0002501567,0.00003860276,0.0002890214,1.601724e-7,0.0001453537,0.00001197093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001022614,"about_ca_system_score_gemma":0.00001773994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007682209,"about_ca_topic_score_gemma":0.000718781,"domain_scores_codex":[0.9982544,0.00002921028,0.0008456416,0.0003112038,0.0003207613,0.0002387846],"domain_scores_gemma":[0.9992247,0.0001936027,0.0001371314,0.0002388228,0.0001183265,0.0000873883],"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":[0.0001322114,0.0001273703,0.002259031,0.000629461,0.0002190609,5.065363e-7,0.003219855,0.9779942,0.0003574921,0.00007449598,0.0009515795,0.01403476],"study_design_scores_gemma":[0.00112927,0.0004148133,0.00183944,0.0004051688,0.0001576813,0.000001082926,0.006510586,0.936773,0.04088677,0.00000278811,0.01145418,0.0004251943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3939601,0.00007375707,0.6005859,0.000005071862,0.002290759,0.001083627,0.0006068094,0.001333634,0.00006032658],"genre_scores_gemma":[0.9981663,0.00009104373,0.0007260382,0.000009034272,0.00004086614,0.0003696938,0.0003515324,0.00005837105,0.0001870911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6042061,"threshold_uncertainty_score":0.9999555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01933386782067357,"score_gpt":0.2207697665624758,"score_spread":0.2014358987418022,"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."}}