{"id":"W4406675972","doi":"10.1016/j.compeleceng.2025.110082","title":"Improving the traffic prediction process efficiency using novel cohesive model","year":2025,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Process (computing); Computer science; Environmental science; Operating system","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":[0.000633145,0.0007284602,0.0008562282,0.0004957112,0.0006978938,0.001067228,0.001400305,0.0009664255,0.001255098],"category_scores_gemma":[0.001833977,0.000420621,0.0006717479,0.0005533108,0.00039923,0.002239808,0.001402847,0.00103272,0.0002964163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004146514,"about_ca_system_score_gemma":0.0009964601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006053805,"about_ca_topic_score_gemma":0.006502082,"domain_scores_codex":[0.9994994,0.00009579712,0.00002929982,0.0001574116,0.0001613535,0.00005673573],"domain_scores_gemma":[0.9993082,0.0002293651,0.00006414225,0.0001356575,0.0002198894,0.00004270722],"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":[0.0001570938,0.0003490697,0.003279673,0.00004832749,0.00009094199,0.00008129767,0.0001146457,0.8801247,0.01278021,0.007016551,0.001029674,0.09492777],"study_design_scores_gemma":[0.000002144915,0.00001745754,0.0000919372,8.205999e-7,0.000005593746,0.00000375271,0.000003357135,0.9989184,0.0004197677,0.0004441968,0.00009036635,0.0000023086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07553659,0.0001754189,0.9217169,0.0001212636,0.00007500253,0.00004154542,0.0000270007,0.0004990865,0.001807285],"genre_scores_gemma":[0.8725607,0.0001661478,0.1250366,0.00009344092,0.00005177171,0.00009135396,0.0001026993,0.00006943363,0.001827793],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006053805,"threshold_uncertainty_score":0.01203716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006585247078503159,"score_gpt":0.2023305125992812,"score_spread":0.195745265520778,"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."}}