{"id":"W4328007215","doi":"10.1109/tvt.2023.3259903","title":"A Novel Traffic Characteristics Aware and Context Prediction Protocol for Intelligent Connected Vehicles","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Canada Research Chairs","keywords":"Context (archaeology); Correctness; Computer science; Protocol (science); Global Positioning System; Floating car data; Real-time computing; Wireless; Digital mapping; Wireless ad hoc network; Wireless network; Traffic congestion; Computer network; Transport engineering; Engineering; Simulation; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001616031,0.0003268718,0.0003756614,0.0006008531,0.0002123661,0.00004089361,0.0001863256,0.0005459171,0.00001905691],"category_scores_gemma":[0.00001524912,0.0003444639,0.0001200808,0.0008035245,0.0001491242,0.00009032016,0.000003329748,0.0005299591,0.00005033131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001340928,"about_ca_system_score_gemma":0.00002943155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002243165,"about_ca_topic_score_gemma":0.00004269575,"domain_scores_codex":[0.9984448,0.00002086616,0.0004304494,0.0004171009,0.0001632812,0.0005235092],"domain_scores_gemma":[0.9992425,0.0001095317,0.00005749547,0.0003744471,0.0001124433,0.000103577],"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":[0.0001101836,0.0001806023,0.00002840159,0.0003304765,0.0002740505,0.00002416274,0.0001071058,0.7944866,0.02128687,0.0001417401,0.0007362867,0.1822935],"study_design_scores_gemma":[0.00168348,0.0003429548,0.0001450102,0.0001670234,0.000068919,0.00009067181,0.0001947875,0.95197,0.02743575,0.00008336743,0.01747628,0.0003417516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2186108,0.00002245667,0.7338576,0.000333863,0.0004454126,0.04199299,0.0002293738,0.004501579,0.000005914502],"genre_scores_gemma":[0.8686906,0.0000444433,0.0005521496,0.00004050491,0.00005913964,0.1304251,0.00003707429,0.0001043986,0.00004665217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7333055,"threshold_uncertainty_score":0.9999008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01967920497557226,"score_gpt":0.248039646603307,"score_spread":0.2283604416277348,"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."}}