{"id":"W2011819199","doi":"10.1155/2013/684741","title":"Estimation Vehicular Waiting Time at Traffic Build-Up Queues","year":2013,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Regina","funders":"Umm Al-Qura University","keywords":"Computer science; Artificial neural network; Discrete wavelet transform; Backpropagation; Hilbert–Huang transform; Real-time computing; Queue; Feature (linguistics); Traffic congestion; Artificial intelligence; Pattern recognition (psychology); Wavelet transform; Wavelet; Computer network; Computer vision","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":[0.0002765835,0.0004132214,0.0003668379,0.001426994,0.000220223,0.0005823794,0.000494935,0.0003014981,0.0007060845],"category_scores_gemma":[0.001593456,0.0001766889,0.0002263467,0.0006755794,0.0000903641,0.0005265692,0.000275001,0.0002501453,0.0002375709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004885808,"about_ca_system_score_gemma":0.0004296547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006609849,"about_ca_topic_score_gemma":0.005054427,"domain_scores_codex":[0.9998226,0.00002027384,0.00001211775,0.0000503852,0.0000490383,0.00004563894],"domain_scores_gemma":[0.9994505,0.0001801878,0.00008012816,0.00002893753,0.000207247,0.00005297208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0019573,0.0003849053,0.2324589,0.0002998149,0.0002056042,0.0004258448,0.0003185134,0.4934844,0.05150347,0.003648307,0.002863889,0.2124491],"study_design_scores_gemma":[0.000008168976,0.00009917362,0.01983402,0.000005739809,0.00002373541,0.00004824582,0.00006204861,0.9729934,0.006245957,0.0004162911,0.0002458127,0.00001745875],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8887884,0.0002850286,0.1075152,0.00005614805,0.00006588669,0.00003400194,0.0004604153,0.0007225656,0.00207226],"genre_scores_gemma":[0.9938673,0.00005591068,0.005348132,0.000004107174,0.000008190151,0.00001046103,0.0002364374,0.00000898144,0.0004604498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006609849,"threshold_uncertainty_score":0.01314276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004337030366601425,"score_gpt":0.2024542183953072,"score_spread":0.1981171880287058,"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."}}