{"id":"W4399903090","doi":"10.1016/j.trc.2024.104719","title":"Calibrating car-following models via Bayesian dynamic regression","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Traffic control and management","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport","keywords":"Bayesian probability; Regression; Bayesian linear regression; Regression analysis; Computer science; Statistics; Econometrics; Bayesian inference; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002177728,0.0007193945,0.001053105,0.001138669,0.0005735906,0.001147911,0.002367902,0.001455755,0.003614778],"category_scores_gemma":[0.00976262,0.0009361125,0.000875323,0.001131499,0.0009386663,0.001833118,0.001455036,0.002231619,0.0007867233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001371361,"about_ca_system_score_gemma":0.001573448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03332799,"about_ca_topic_score_gemma":0.02174371,"domain_scores_codex":[0.999272,0.000246813,0.00003222505,0.000220718,0.0001422309,0.0000859147],"domain_scores_gemma":[0.9970887,0.001663307,0.0004027244,0.0002930801,0.0004405087,0.0001116786],"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":[0.00001106277,0.00001339389,0.0008047683,0.00001247137,0.00001289734,0.00001204833,0.00001767188,0.9862679,0.0001822813,0.00747541,0.0003131287,0.00487694],"study_design_scores_gemma":[0.000002780959,0.00000300432,0.0001677612,0.000005163202,0.000002668558,0.000004573184,0.000003821614,0.9962368,0.00008669671,0.003232509,0.0002481533,0.000006160843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06890707,0.0002831348,0.9246547,0.00039665,0.00004557544,0.00006533363,0.0005344487,0.0009622531,0.004150798],"genre_scores_gemma":[0.8826078,0.0003868614,0.1109446,0.0001469559,0.00004406349,0.0001606815,0.001282953,0.0003825328,0.004043486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03332799,"threshold_uncertainty_score":0.06626803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02687742098150629,"score_gpt":0.3101360990624183,"score_spread":0.283258678080912,"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."}}