{"id":"W3031167913","doi":"10.1109/tits.2020.2994280","title":"A Novel VANET-Assisted Traffic Control for Supporting Vehicular Cloud Computing","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicular ad hoc network; Computer science; Intersection (aeronautics); Cloud computing; Wireless ad hoc network; Correctness; Intelligent transportation system; Computer network; Traffic flow (computer networking); Wireless; Distributed computing; Engineering; Transport engineering; Telecommunications; Algorithm","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":[0.0003150167,0.0004159136,0.0004904388,0.0005672083,0.0007280386,0.0009678939,0.001685934,0.000418269,0.001694427],"category_scores_gemma":[0.0008006277,0.0001846139,0.0003342598,0.0007025365,0.0003232327,0.0008110738,0.0009187677,0.000470298,0.000314842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007155876,"about_ca_system_score_gemma":0.001196971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006026474,"about_ca_topic_score_gemma":0.007277993,"domain_scores_codex":[0.9996299,0.00005681894,0.00001890039,0.00007808471,0.0001312716,0.00008499943],"domain_scores_gemma":[0.9996576,0.00005130987,0.00003396447,0.00005026982,0.0001524438,0.00005446115],"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":[0.0004059155,0.0003113407,0.003330457,0.0001672371,0.0001189772,0.0005677525,0.0001056278,0.6660773,0.04188852,0.05051655,0.01027992,0.2262304],"study_design_scores_gemma":[0.00001092898,0.00004362437,0.000128051,0.000003823578,0.00000717096,0.00005973934,0.00001163918,0.9930894,0.002271292,0.001255578,0.003110417,0.000008407217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03548903,0.0004519485,0.9550162,0.000320119,0.0002666542,0.0001437841,0.0001309739,0.001386277,0.006795092],"genre_scores_gemma":[0.8842824,0.0002436146,0.1116938,0.0001686557,0.0000650733,0.0000925983,0.0001547792,0.00006254661,0.003236594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006026474,"threshold_uncertainty_score":0.0119828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02492144693151888,"score_gpt":0.2405751585929427,"score_spread":0.2156537116614238,"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."}}