{"id":"W4402156646","doi":"10.1109/icc51166.2024.10622557","title":"SK-SVR-CNN: A Hybrid Approach for Traffic Flow Prediction with Signature PDE Kernel and Convolutional Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Convolutional neural network; Computer science; Signature (topology); Kernel (algebra); Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007064469,0.00105917,0.0008193904,0.000803904,0.000241726,0.000566269,0.001724489,0.0009102421,0.001505511],"category_scores_gemma":[0.001382252,0.0004460489,0.0006006898,0.0008941333,0.0003034564,0.001243132,0.0009165669,0.001246284,0.000715454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006713364,"about_ca_system_score_gemma":0.001038838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01581429,"about_ca_topic_score_gemma":0.01851808,"domain_scores_codex":[0.999763,0.00004324094,0.00001146091,0.00007659089,0.00006085413,0.00004492831],"domain_scores_gemma":[0.9996295,0.0001042569,0.00003884941,0.00005612051,0.0001436946,0.0000275554],"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":[0.0001724612,0.0002165235,0.002955339,0.00008360152,0.0001517272,0.000103763,0.00003520331,0.6894296,0.007617598,0.003949471,0.006172765,0.2891119],"study_design_scores_gemma":[0.000001103369,0.000006475263,0.00006633015,0.000001336069,0.000003196842,0.000004320071,0.000001170841,0.9990639,0.0003807718,0.0002874754,0.00018228,0.000001699623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05016109,0.0008437152,0.9413739,0.0003717668,0.0001606776,0.0000653303,0.0003837158,0.004187031,0.002452904],"genre_scores_gemma":[0.7307051,0.0006355262,0.2568042,0.0003213015,0.0001615443,0.0001120798,0.001721225,0.0002914672,0.009247607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01581429,"threshold_uncertainty_score":0.03144449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005264481432693022,"score_gpt":0.1788466559873298,"score_spread":0.1735821745546368,"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."}}