{"id":"W2900730974","doi":"10.1109/igarss.2018.8518853","title":"Traffic Flow Prediction Based on Cascaded Artificial Neural Network","year":2018,"lang":"en","type":"article","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial neural network; Traffic flow (computer networking); Task (project management); Data mining; Real-time computing; Artificial intelligence; Flow (mathematics); Machine learning; Engineering; Computer network","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":[0.0001053948,0.0001243104,0.00008763094,0.00009227235,0.00009342688,0.00003462555,0.00008533538,0.00007887761,0.0001811507],"category_scores_gemma":[0.000005079965,0.0001182267,0.00004826948,0.0001926508,0.00003438843,0.00007965264,0.000008710262,0.0001147637,0.0001002613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003796487,"about_ca_system_score_gemma":0.000004233181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001827071,"about_ca_topic_score_gemma":0.00003437229,"domain_scores_codex":[0.9992924,0.00001650115,0.000166168,0.0001528035,0.0001436275,0.0002284942],"domain_scores_gemma":[0.9997124,0.00001497159,0.00001137426,0.0001827041,0.00001808059,0.00006045917],"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":[0.00001703919,0.00001846513,0.000008413862,0.00000591715,0.000009012168,0.000001533036,0.00001934432,0.6620588,0.0001033225,0.0003227494,0.2728375,0.06459785],"study_design_scores_gemma":[0.0001408027,0.0001524145,0.0007657722,0.00001140312,0.0000112124,9.431412e-7,0.000008139819,0.974546,0.0006130142,0.00001787354,0.02362939,0.0001030266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1465749,0.00002071916,0.6838326,0.0005462387,0.00586381,0.0007746954,0.00002354827,0.05525269,0.1071108],"genre_scores_gemma":[0.9960285,0.000004249474,0.002318069,0.0004054858,0.00106894,0.000028338,0.00002036572,0.0000253722,0.0001006804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8494536,"threshold_uncertainty_score":0.4821145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01215261045755988,"score_gpt":0.2072735147908807,"score_spread":0.1951209043333208,"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."}}