{"id":"W4313594835","doi":"10.1016/j.amar.2022.100265","title":"An empirical investigation of driver car-following risk evolution using naturistic driving data and random parameters multinomial logit model with heterogeneity in means and variances","year":2023,"lang":"en","type":"article","venue":"Analytic Methods in Accident Research","topic":"Traffic and Road Safety","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Multinomial logistic regression; Crash; Poison control; Econometrics; Driving factors; Computer science; Transport engineering; Engineering; Economics; Geography; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004447468,0.0002517442,0.0003736079,0.0009305519,0.0003974796,0.0009467184,0.001079296,0.0008650001,0.00220172],"category_scores_gemma":[0.02437973,0.0002734966,0.0006835203,0.001131186,0.0005087352,0.001479754,0.000563428,0.0008800329,0.0002581912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008043611,"about_ca_system_score_gemma":0.0003670155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01699707,"about_ca_topic_score_gemma":0.01130522,"domain_scores_codex":[0.9989741,0.0006023319,0.00005886492,0.0001736156,0.00009015867,0.0001010667],"domain_scores_gemma":[0.9541046,0.03938119,0.003048861,0.001954926,0.000980405,0.0005299913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007864743,0.0007250078,0.8636484,0.0001214606,0.0003263747,0.0008572889,0.002043957,0.09730034,0.001482907,0.01161338,0.0006600434,0.02043432],"study_design_scores_gemma":[0.00003398032,0.0003933077,0.393003,0.00002308408,0.0001989005,0.0006071488,0.002480099,0.5976727,0.001000121,0.003709613,0.0008031583,0.00007492363],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981932,0.00003206274,0.001438361,0.00003341883,0.000001051678,0.000006370831,0.00008204728,0.000009480667,0.0002039073],"genre_scores_gemma":[0.9990281,0.00001779913,0.0005006053,0.000003483633,0.000001204808,0.00000308855,0.0001675735,0.000003073125,0.0002751384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01699707,"threshold_uncertainty_score":0.03379625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1766141123090785,"score_gpt":0.4698554035658559,"score_spread":0.2932412912567773,"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."}}