{"id":"W2784328206","doi":"10.1155/2018/8645709","title":"Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas from the Shanghai Naturalistic Driving Study","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Transport engineering; Incentive; Traffic flow (computer networking); Crash; Mixed logit; Variance (accounting); Computer science; Logistic regression; Engineering; Business; Computer security; Economics","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":[0.0006592781,0.0004377321,0.0002498592,0.0007322259,0.0002076519,0.0003753565,0.0004787523,0.0002879619,0.0009186197],"category_scores_gemma":[0.001347551,0.0002090359,0.0006485228,0.0005036564,0.0002766209,0.0002401052,0.0003187271,0.0002077091,0.000129898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008182041,"about_ca_system_score_gemma":0.000621498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1068565,"about_ca_topic_score_gemma":0.1188622,"domain_scores_codex":[0.9997622,0.0001019346,0.00001140959,0.00005961147,0.00001893817,0.0000458262],"domain_scores_gemma":[0.999144,0.0003765662,0.0001639874,0.00009860165,0.0001169389,0.00009991008],"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":[0.0004116273,0.0004180244,0.7696158,0.0001025567,0.0002116894,0.000759978,0.001008825,0.2114841,0.002502732,0.001025045,0.0008919498,0.01156759],"study_design_scores_gemma":[0.00002430486,0.0002064895,0.430439,0.00001432118,0.00005945132,0.0001091389,0.0007424604,0.5668408,0.0004065485,0.0004676191,0.0006584969,0.00003143639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986305,0.00001938864,0.0009153619,0.00001075194,0.000001421386,0.0000110767,0.0002855597,0.00001082686,0.0001151484],"genre_scores_gemma":[0.9980415,0.00001989328,0.0007590877,0.00000353283,0.000001616895,0.00001830042,0.0009895042,0.000002292881,0.0001642035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1068565,"threshold_uncertainty_score":0.212469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0088267914782041,"score_gpt":0.2306886127829184,"score_spread":0.2218618213047143,"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."}}