{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007779418,0.0006923553,0.0007432073,0.001260413,0.00083296,0.0007816445,0.001718358,0.0005785024,0.0008651562],"category_scores_gemma":[0.002099982,0.0002417084,0.0002934973,0.001026749,0.0004729056,0.001333552,0.001575525,0.0008950705,0.0002915276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00117806,"about_ca_system_score_gemma":0.001727482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006157714,"about_ca_topic_score_gemma":0.006637481,"domain_scores_codex":[0.999464,0.0001006609,0.00004200536,0.00009488704,0.0002054952,0.00009285328],"domain_scores_gemma":[0.9990991,0.0002725635,0.0001033,0.00009506595,0.0003731383,0.00005681494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009116694,0.0004289186,0.003061653,0.000471105,0.0001527855,0.0008769529,0.000440038,0.2923148,0.04189114,0.04674737,0.029138,0.5835656],"study_design_scores_gemma":[0.00004522085,0.0002209888,0.0003743822,0.00001837635,0.00003762615,0.0002954769,0.00006236366,0.9707038,0.009032921,0.004545525,0.01461616,0.00004707461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04443378,0.001880057,0.9403391,0.0005097539,0.0005260421,0.0006006429,0.000512163,0.003901048,0.00729738],"genre_scores_gemma":[0.8563766,0.001103852,0.1333091,0.00035796,0.0001407409,0.0005451931,0.001218726,0.000110357,0.006837317],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006157714,"threshold_uncertainty_score":0.01224375,"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."}}