{"id":"W576447651","doi":"","title":"Study on Calibration and Validation of Fundamental Diagram for Urban Arterials","year":2012,"lang":"en","type":"article","venue":"Transportation Research Board 91st Annual MeetingTransportation Research Board","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Diagram; Flow (mathematics); Traffic flow (computer networking); Calibration; Maximum flow problem; Data collection; Statistics; Simulation; Statistical physics; Mechanics; Environmental science; Meteorology; Computer science; Mathematics; Physics; Mathematical optimization","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":[0.004596964,0.0002872008,0.000383741,0.001090839,0.0003797473,0.0001079515,0.0002472471,0.0001866303,0.00004292551],"category_scores_gemma":[0.0001242582,0.0002999746,0.000110401,0.0008506062,0.0002924677,0.0009353529,0.000007968354,0.0005231219,0.000008750464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001169611,"about_ca_system_score_gemma":0.00005302043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003843671,"about_ca_topic_score_gemma":0.0004871819,"domain_scores_codex":[0.9954201,0.00047628,0.0009125996,0.0004636118,0.00181794,0.0009094797],"domain_scores_gemma":[0.9978495,0.0006110336,0.0001048463,0.0003312347,0.0007572036,0.0003462135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00278491,0.004335676,0.7176392,0.002848688,0.0008095227,0.00002791355,0.08959228,0.005069923,0.048498,0.03121108,0.08164848,0.01553434],"study_design_scores_gemma":[0.003374989,0.002929981,0.9016551,0.0002593148,0.000110355,2.783055e-7,0.02748339,0.002121061,0.0536153,0.0003604554,0.007446457,0.0006433],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842843,0.0001170016,0.009367551,0.0001756629,0.0003005329,0.003816355,0.0004592651,0.0009518981,0.000527391],"genre_scores_gemma":[0.996677,0.0001768597,0.0008497532,0.00001597263,0.0002348877,0.00136845,0.0005078103,0.00008178744,0.0000875253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1840159,"threshold_uncertainty_score":0.9999452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07486028133228853,"score_gpt":0.3729425012736582,"score_spread":0.2980822199413697,"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."}}