{"id":"W3004839142","doi":"10.1109/tsusc.2020.2971628","title":"DACON: A Novel Traffic Prediction and Data-Highway-Assisted Content Delivery Protocol for Intelligent Vehicular Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Sustainable Computing","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Canada Research Chairs","keywords":"Vehicular ad hoc network; Computer science; Protocol (science); Traffic flow (computer networking); Computer network; Scheme (mathematics); Intelligent transportation system; Service (business); Floating car data; Transport engineering; Wireless ad hoc network; Engineering; Traffic congestion; Telecommunications; Wireless","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":[0.0004039257,0.0003848814,0.0003964838,0.0001226069,0.0004275901,0.0001841049,0.000345069,0.0002039652,0.00001402165],"category_scores_gemma":[0.00001657702,0.000410818,0.0001264571,0.00043311,0.00005925185,0.000338622,0.00001661124,0.0005591285,0.000003162798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002548524,"about_ca_system_score_gemma":0.00006972002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001721741,"about_ca_topic_score_gemma":0.00001099717,"domain_scores_codex":[0.9977017,0.00005391972,0.0005808654,0.0006650255,0.0002355826,0.0007628866],"domain_scores_gemma":[0.998737,0.0001994873,0.00008770292,0.0004635627,0.0002170363,0.0002951915],"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":[0.0001548987,0.0001074389,0.000003114609,0.00060115,0.0001919521,0.0000263718,0.0002511712,0.959818,0.0003362462,0.00002108625,0.0007846846,0.03770389],"study_design_scores_gemma":[0.002009709,0.0002723623,0.00003213379,0.0001513138,0.0001127146,0.00003675579,0.0008459688,0.9822567,0.0007454988,0.000002155267,0.01318491,0.0003498135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01210226,0.00008606553,0.944294,0.0001396039,0.0002679525,0.04211774,0.00005056248,0.0009098715,0.0000319473],"genre_scores_gemma":[0.9799498,0.00001344846,0.003872541,0.0001986382,0.0003496667,0.0153869,0.00006119911,0.000116306,0.00005147714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9678476,"threshold_uncertainty_score":0.9998344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06424917621630624,"score_gpt":0.2678563449260455,"score_spread":0.2036071687097393,"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."}}