{"id":"W3008638339","doi":"10.1155/2020/7523423","title":"Development and Validation of Improved Impedance Functions for Roads with Mixed Traffic Using Taxi GPS Trajectory Data and Simulation","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; Wuhan University of Technology; Wuhan University; National Natural Science Foundation of China","keywords":"Electrical impedance; Computer science; Function (biology); Global Positioning System; Trajectory; Transport engineering; Traffic flow (computer networking); Business process reengineering; Simulation; Telecommunications; Engineering; Physics; Computer security; Electrical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.00009986119,0.00008020762,0.0001340217,0.00007319905,0.00003375707,0.000009206708,0.00004451734,0.0000289329,5.853916e-7],"category_scores_gemma":[0.000009573571,0.00007593624,0.00001588354,0.00009235692,0.00001443182,0.0005296522,0.000001537224,0.00005949347,1.682289e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001759179,"about_ca_system_score_gemma":0.00002151567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.840785e-7,"about_ca_topic_score_gemma":0.00001120414,"domain_scores_codex":[0.9994044,0.000005736348,0.0003374657,0.00009566846,0.00009601684,0.00006068534],"domain_scores_gemma":[0.99963,0.00002526074,0.0001689112,0.00005439972,0.00007687619,0.00004458932],"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":[0.0001091403,0.00001193121,0.00008936849,0.0001750924,0.00005083579,5.244792e-7,0.001172633,0.9086446,0.02538642,0.000006527143,0.00001724251,0.06433571],"study_design_scores_gemma":[0.0018648,0.0002494094,0.01450391,0.0001054751,0.0001912111,0.000002923294,0.001157973,0.9645631,0.01456162,0.000005318061,0.002641311,0.0001529378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5306556,0.00008502453,0.4689892,0.00001318236,0.00004567417,0.0001289793,0.00001516152,0.00006599718,0.000001178187],"genre_scores_gemma":[0.8941643,0.0000607157,0.1056583,0.000006994154,0.0000276562,0.000003704337,0.00006506574,0.00001257929,6.873717e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3635086,"threshold_uncertainty_score":0.309659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02628710376987429,"score_gpt":0.252201041477016,"score_spread":0.2259139377071417,"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."}}