{"id":"W2345370719","doi":"","title":"Applicability Analysis of a Macroscopic Traffic Flow Model in Traffic State Prediction","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bottleneck; Traffic flow (computer networking); Microscopic traffic flow model; Calibration; Computer science; Traffic generation model; Field (mathematics); Traffic congestion reconstruction with Kerner's three-phase theory; Simulation; Replicate; Traffic congestion; Flow (mathematics); Engineering; Transport engineering; Real-time computing; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005149398,0.0004213333,0.0007860445,0.004274348,0.0003590118,0.00005468932,0.0006512425,0.0003273945,0.0001255677],"category_scores_gemma":[0.000135221,0.0003840476,0.0003452505,0.005983224,0.0007092165,0.0007895524,0.00001679717,0.001034061,0.00001998056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004250655,"about_ca_system_score_gemma":0.0002223525,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006038217,"about_ca_topic_score_gemma":0.01950757,"domain_scores_codex":[0.992273,0.0005457043,0.001748483,0.001026093,0.002951541,0.00145518],"domain_scores_gemma":[0.9966682,0.0006465174,0.0001286813,0.0007438175,0.001347634,0.000465111],"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":[0.0005196662,0.0003835801,0.0184039,0.0005522404,0.0004261216,0.00002622701,0.007544,0.9338409,0.005587108,0.0004215368,0.002388489,0.02990619],"study_design_scores_gemma":[0.001951028,0.0003382661,0.3557912,0.0002796532,0.0001669262,8.868425e-8,0.00141544,0.6361322,0.00202553,0.0002673099,0.001203637,0.0004287226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9411725,0.000092735,0.05211489,0.0003144249,0.00009697028,0.001905053,0.002117416,0.001847993,0.0003379594],"genre_scores_gemma":[0.9942942,0.001140164,0.002884074,0.00001382442,0.00003597231,0.0009654809,0.0003843057,0.00009045218,0.0001914821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3373872,"threshold_uncertainty_score":0.9998611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03230212122052486,"score_gpt":0.3317965662394564,"score_spread":0.2994944450189315,"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."}}