{"id":"W4386279768","doi":"10.1155/2023/3363057","title":"A Cooperative Lane-Changing Strategy for Weaving Sections of Urban Expressway under the Connected Autonomous Vehicle Environment","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Key Technology Research and Development Program of Shandong","keywords":"Weaving; Benchmark (surveying); Transport engineering; Computer science; Control (management); Automotive engineering; Engineering; Simulation; 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":[0.0001185576,0.00007583067,0.0001303903,0.0001046715,0.00006380254,0.000008914834,0.00005695221,0.00002246021,0.00001035013],"category_scores_gemma":[0.000003736503,0.00005981502,0.00007041913,0.0001448853,0.00001330272,0.0001400627,0.00000120804,0.00008355992,0.000001029191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003604187,"about_ca_system_score_gemma":0.00001178059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001803799,"about_ca_topic_score_gemma":0.00003256643,"domain_scores_codex":[0.999409,0.00001023561,0.0002911006,0.00005904563,0.0001048929,0.0001256922],"domain_scores_gemma":[0.9996807,0.0000895421,0.000101286,0.00005908826,0.00004376316,0.00002559824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00003504871,0.00001436983,0.00001417689,0.00003294693,0.00008649238,0.000002821795,0.002425021,0.9638053,0.02983826,0.0009660468,0.00005773627,0.002721774],"study_design_scores_gemma":[0.01543115,0.001979887,0.5614901,0.0005922222,0.001058063,0.00001639656,0.09862851,0.2488931,0.04543467,0.002969098,0.02241542,0.001091351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9039479,0.0002250349,0.09502757,0.0001339195,0.000250361,0.0002767119,0.00002849593,0.0000679687,0.00004206515],"genre_scores_gemma":[0.9991237,0.0001007888,0.0005806627,0.000009562374,0.00005813804,0.00002541437,0.00001858867,0.0000145652,0.00006859878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7149122,"threshold_uncertainty_score":0.2439186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01066871636808264,"score_gpt":0.2168121419153508,"score_spread":0.2061434255472682,"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."}}