{"id":"W2982294315","doi":"10.1109/wcnc.2019.8885854","title":"TVDR: A Novel Traffic Volume Aware Data Routing Protocol for Vehicular Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Computer network; Routing protocol; Vehicular ad hoc network; Protocol (science); Traffic flow (computer networking); Wireless; Distributed computing; Network topology; Routing (electronic design automation); Wireless ad hoc network; Telecommunications","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":[0.0009542088,0.0007070825,0.0007602815,0.001350856,0.000662133,0.001075599,0.002034287,0.0007402697,0.0005866292],"category_scores_gemma":[0.002743423,0.0002636703,0.0005370493,0.00121666,0.0006694018,0.001633971,0.001813731,0.0009961235,0.0002656947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008513287,"about_ca_system_score_gemma":0.001065575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001670713,"about_ca_topic_score_gemma":0.00196609,"domain_scores_codex":[0.9988641,0.0002870366,0.00009161562,0.0001777718,0.0004463003,0.0001333167],"domain_scores_gemma":[0.9989086,0.0003997993,0.0002025225,0.0001308156,0.0002965689,0.00006157642],"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":[0.0005050584,0.0001541114,0.001375681,0.0006945948,0.0002384217,0.000684601,0.0006229494,0.3825881,0.06558305,0.1025365,0.01459516,0.4304218],"study_design_scores_gemma":[0.00006529662,0.00041706,0.0003364875,0.00005535165,0.00009702888,0.000706781,0.0001417319,0.9201506,0.01565382,0.02511745,0.03715498,0.0001034298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01278296,0.001551748,0.9803375,0.0003384837,0.0002294322,0.0002113671,0.0001670258,0.001127676,0.00325389],"genre_scores_gemma":[0.7136211,0.002931254,0.2756471,0.0005139419,0.0003033634,0.0006659178,0.001037114,0.0001812176,0.005099007],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002034287,"threshold_uncertainty_score":0.006176889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02767367487317262,"score_gpt":0.2641579375403838,"score_spread":0.2364842626672112,"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."}}