{"id":"W3097390569","doi":"10.1155/2020/8897141","title":"Calibration of Microscopic Traffic Flow Simulation Models considering Subsets of Links and Parameters","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Calibration; Selection (genetic algorithm); Set (abstract data type); Computer science; Traffic flow (computer networking); Microscopic traffic flow model; Traffic simulation; Mathematical optimization; Mathematical model; Flow (mathematics); Data mining; Simulation; Traffic generation model; Algorithm; Machine learning; Real-time computing; Engineering; Mathematics; Statistics; Microsimulation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002596566,0.0009148669,0.0007485674,0.000831308,0.0004473502,0.0009066491,0.001031077,0.0007218617,0.0006122406],"category_scores_gemma":[0.008935906,0.0007111084,0.0006775794,0.0006865293,0.0005570942,0.001290143,0.001216411,0.001335843,0.00008981199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001222975,"about_ca_system_score_gemma":0.00152813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004247303,"about_ca_topic_score_gemma":0.003138602,"domain_scores_codex":[0.998462,0.0006561253,0.00006855426,0.0002932994,0.0004132028,0.0001067621],"domain_scores_gemma":[0.9963272,0.002104835,0.0006003568,0.000539617,0.0003631354,0.000064903],"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":[0.00001205643,0.000027348,0.0007196243,0.000009693633,0.00001333374,0.000009303189,0.00001928543,0.9898583,0.001052294,0.001464994,0.00003943524,0.006774237],"study_design_scores_gemma":[0.000003613582,0.00002387647,0.0002842076,0.000004016345,0.00000605287,0.000008777777,0.00001130456,0.9969001,0.001274085,0.001249504,0.0002282725,0.000006164123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1102303,0.00004383153,0.8872252,0.00009049103,0.00001345111,0.00009196313,0.0000611595,0.0002826134,0.001961006],"genre_scores_gemma":[0.8759152,0.00008470072,0.1230256,0.00002821238,0.000008057437,0.0001801281,0.000133612,0.00004726144,0.0005772483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004247303,"threshold_uncertainty_score":0.01373214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01428448057290469,"score_gpt":0.2171550497153438,"score_spread":0.2028705691424391,"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."}}