{"id":"W2295859842","doi":"","title":"Calibration of Driving Behavior Models using Derivative-Free Optimization and Video Data for Montreal Highways","year":2016,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Traffic control and management","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"VisSim; Calibration; Software; Flexibility (engineering); Traffic simulation; Computer science; Field (mathematics); Simulation; Intelligent transportation system; Real-time computing; Microsimulation; Engineering; Transport engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002500969,0.0002097964,0.000260973,0.0002053835,0.00008428251,0.00005739631,0.0003993288,0.0001226212,0.000004542763],"category_scores_gemma":[0.00008199652,0.0001824747,0.00004776856,0.0001377087,0.00004242676,0.0009056042,0.0002635639,0.00006122678,8.444847e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000138609,"about_ca_system_score_gemma":0.0000350521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001047086,"about_ca_topic_score_gemma":0.002124878,"domain_scores_codex":[0.9988106,0.00002703428,0.0003726926,0.0003100463,0.0001566098,0.0003230457],"domain_scores_gemma":[0.9987685,0.0001072546,0.0001058883,0.0008535073,0.00005978238,0.0001050455],"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":[0.00003368232,0.00006770956,0.00104796,0.00007645223,0.00006954726,0.00000334215,0.00009224239,0.9163656,0.03467008,0.00608527,0.001245185,0.04024289],"study_design_scores_gemma":[0.0009243895,0.00004090154,0.002917974,0.00007014254,0.00009385862,0.000005325561,0.00002145475,0.9924685,0.002438651,0.0007241974,0.00007855003,0.0002160052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04341179,0.0003633686,0.9538757,0.0003563815,0.00007919875,0.0008853925,0.0002822585,0.0007067673,0.00003919259],"genre_scores_gemma":[0.8115304,0.0001856515,0.1878087,0.00003754753,0.00005386358,0.000248983,0.00005148019,0.00005257169,0.00003078107],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7681186,"threshold_uncertainty_score":0.7441103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02023102787357144,"score_gpt":0.2215138863357333,"score_spread":0.2012828584621618,"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."}}