{"id":"W4400645230","doi":"10.1109/iv55156.2024.10588568","title":"Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression","year":2024,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Bayesian probability; Computer science; Artificial intelligence; Regression; Regression analysis; Pattern recognition (psychology); Statistics; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008875849,0.0001008057,0.0001296991,0.0001461927,0.001118071,0.000281995,0.0001404336,0.0001328581,0.001780154],"category_scores_gemma":[0.00009774335,0.00008559012,0.0002190769,0.0006158994,0.00007974908,0.0002972109,0.00002517098,0.000273346,0.00005722594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001649606,"about_ca_system_score_gemma":0.0002514825,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01469766,"about_ca_topic_score_gemma":0.005843522,"domain_scores_codex":[0.9986458,0.0002490157,0.0001843321,0.0002645278,0.0003894187,0.0002669449],"domain_scores_gemma":[0.9995946,0.0001056774,0.00002937301,0.0001179578,0.0000397107,0.0001126434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004260461,0.0003649869,0.3523911,0.0004892126,0.0004375899,0.0006612964,0.3008058,0.02625351,0.03628321,0.03145675,0.003609723,0.2472042],"study_design_scores_gemma":[0.001036761,0.0002669519,0.006711391,0.002570403,0.003070182,0.00001328654,0.3096136,0.4194781,0.006392308,0.003192227,0.2440824,0.003572443],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9754568,0.0004960922,0.01213248,0.0007598763,0.0004296119,0.0001202792,0.000001234001,0.0003532818,0.01025037],"genre_scores_gemma":[0.9869053,0.00001346036,0.0005758065,0.00003358448,0.000275749,0.000003994004,0.00001073716,0.00001201793,0.01216938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3932246,"threshold_uncertainty_score":0.9991323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01492116662027991,"score_gpt":0.3480778261979117,"score_spread":0.3331566595776318,"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."}}