{"id":"W1515133042","doi":"10.1002/atr.1217","title":"Applying multiple kernel learning and support vector machine for solving the multicriteria and nonlinearity problems of traffic flow prediction","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Computer science; Kernel (algebra); Nonlinear system; Flow (mathematics); Machine learning; Kernel method; Traffic flow (computer networking); Artificial intelligence; Mathematical optimization; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000299711,0.00008727325,0.0001409151,0.00007362185,0.00006573827,0.00001114055,0.0000333175,0.00003918248,0.000001657857],"category_scores_gemma":[0.00002320405,0.00007103134,0.00004273604,0.00005295212,0.00002280429,0.000417529,0.000001717367,0.0001468635,3.670116e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001464848,"about_ca_system_score_gemma":0.000004260894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001156475,"about_ca_topic_score_gemma":0.00001515161,"domain_scores_codex":[0.9993647,0.000009074414,0.0003517021,0.00005761365,0.0001026111,0.0001142566],"domain_scores_gemma":[0.999662,0.00006191185,0.0001364598,0.00003503883,0.00005782125,0.00004671439],"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":[0.000141281,0.00009175709,0.01338613,0.0008908987,0.0001160103,9.288098e-7,0.006108323,0.7035687,0.06059716,0.00003827899,0.00007074299,0.2149898],"study_design_scores_gemma":[0.002607163,0.0003544223,0.194946,0.0002271321,0.0002155819,0.00002057429,0.0009591162,0.7873667,0.004084505,0.00001817956,0.009021482,0.0001791134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7475232,0.0007755742,0.2505249,0.0000262637,0.0003440148,0.0005523943,0.00003200417,0.0002133485,0.000008284031],"genre_scores_gemma":[0.9741189,0.000690434,0.02502016,0.000005161327,0.0000794134,0.00003831361,0.00002776571,0.00001652938,0.000003311348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2265957,"threshold_uncertainty_score":0.2896574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008694356383849518,"score_gpt":0.2303106298280279,"score_spread":0.2216162734441784,"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."}}