{"id":"W2901248954","doi":"10.1155/2018/6372861","title":"The Roles of Car Following and Lane Changing Drivers’ Anticipations during Vehicle Inserting Process: A Structural Equation Model Approach","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Key Research and Development Projects of Shaanxi Province; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Structural equation modeling; Anticipation (artificial intelligence); Kinematics; Vehicle dynamics; Process (computing); Computer science; Automotive engineering; Transport engineering; Simulation; Engineering; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001070062,0.00006401057,0.0001048245,0.00008555462,0.0001242197,0.00001248052,0.00004882646,0.00001781366,3.693363e-7],"category_scores_gemma":[0.000009912834,0.00005058234,0.00004070962,0.0001134742,0.00001644946,0.0003229833,0.000001362134,0.00006398572,4.851023e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000176219,"about_ca_system_score_gemma":0.00000829178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.116433e-7,"about_ca_topic_score_gemma":0.00003121723,"domain_scores_codex":[0.9994148,0.000005643534,0.0002753116,0.00005219522,0.0001418662,0.0001102084],"domain_scores_gemma":[0.9997073,0.00001695824,0.0001333554,0.00004112055,0.00007815255,0.00002307752],"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":[0.00003050134,0.000004230661,0.0007470155,0.00007799742,0.0000400874,0.000001253666,0.01299804,0.9551377,0.02617237,0.0001751324,1.539461e-7,0.004615513],"study_design_scores_gemma":[0.001426813,0.00006716371,0.1362212,0.0001302869,0.000134058,0.000002459786,0.007573022,0.8497416,0.004049173,0.0005354694,0.000003949103,0.0001147704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9664562,0.000226531,0.03307329,0.00002041673,0.00008671189,0.00008881341,0.000001633238,0.00002183871,0.00002460679],"genre_scores_gemma":[0.996704,0.00003289077,0.003194212,0.000002668827,0.00004778303,0.00000332627,0.000003086504,0.000009054771,0.000002998216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1354742,"threshold_uncertainty_score":0.2062688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008110445891349688,"score_gpt":0.2227586691121206,"score_spread":0.2146482232207709,"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."}}