{"id":"W4293255345","doi":"10.1109/tvt.2022.3151806","title":"Latency Minimization of Reverse Offloading in Vehicular Edge Computing","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Minification; Computer science; Edge computing; Latency (audio); Computer network; Enhanced Data Rates for GSM Evolution; Embedded system; Real-time computing; Artificial intelligence; 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":[0.0004278814,0.0009286808,0.0006925025,0.0003008796,0.0006482747,0.0009717941,0.0008828683,0.0004554479,0.001417107],"category_scores_gemma":[0.001305301,0.0002893109,0.0003785255,0.00056075,0.000496678,0.0009746334,0.0009955675,0.0004545669,0.0001919006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008162168,"about_ca_system_score_gemma":0.001287301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00470867,"about_ca_topic_score_gemma":0.005038845,"domain_scores_codex":[0.9993591,0.0001372164,0.00002828159,0.0001279579,0.0001317069,0.0002157146],"domain_scores_gemma":[0.9995635,0.0002077268,0.00005046109,0.00004181186,0.00009433398,0.00004218897],"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":[0.0002151941,0.00007052171,0.0008543893,0.000132874,0.00002402914,0.0001580795,0.0001096319,0.9236496,0.007886869,0.01198671,0.001643616,0.0532684],"study_design_scores_gemma":[0.000005492241,0.00003574471,0.0001004575,0.000003956584,0.000005871951,0.00002985466,0.00003503283,0.9958667,0.0009703225,0.002654592,0.0002871304,0.000004981142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09956929,0.0008573349,0.8915711,0.000279663,0.00007066122,0.00007168306,0.00006277054,0.0002951057,0.007222317],"genre_scores_gemma":[0.9696525,0.0002567066,0.02822202,0.00005636794,0.00001937744,0.0000531499,0.00005405159,0.00003501901,0.001650859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00470867,"threshold_uncertainty_score":0.009362519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01119937483334465,"score_gpt":0.2241252994243139,"score_spread":0.2129259245909693,"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."}}