{"id":"W3040977518","doi":"10.1145/3392064","title":"Computation Offloading and Retrieval for Vehicular Edge Computing","year":2020,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Edge computing; Cloud computing; Distributed computing; Computation offloading; Edge device; Mobile edge computing; Enhanced Data Rates for GSM Evolution; Mobile device; Computation; Vehicular ad hoc network; Bandwidth (computing); Scheduling (production processes); Computer network; Wireless; Wireless ad hoc network; Artificial intelligence","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":[0.0004796396,0.0005263501,0.00067565,0.001858708,0.0003847864,0.001412693,0.001018636,0.0007475064,0.002184354],"category_scores_gemma":[0.001222796,0.0002820615,0.0005840754,0.002607846,0.0003718944,0.002141772,0.0006957647,0.0009160396,0.001519641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006134611,"about_ca_system_score_gemma":0.0009663813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00102601,"about_ca_topic_score_gemma":0.001231326,"domain_scores_codex":[0.9995242,0.00008229422,0.00003976416,0.00007215943,0.0002245901,0.00005688125],"domain_scores_gemma":[0.9995016,0.000219511,0.00003455631,0.00004762753,0.000175236,0.00002141347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005723796,0.00006269471,0.0005025025,0.003782975,0.0000467434,0.0001798775,0.0001144557,0.003356393,0.004273506,0.04026264,0.01513566,0.9322254],"study_design_scores_gemma":[0.00001875657,0.0002257888,0.001541636,0.002336836,0.0001381729,0.002598555,0.0003199337,0.02097078,0.008165222,0.03087942,0.9327356,0.00006929928],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.004602188,0.8940449,0.07316838,0.001341853,0.001000954,0.0001082195,0.00006674269,0.0001872879,0.02547944],"genre_scores_gemma":[0.07353888,0.8794309,0.03402935,0.0008061222,0.001238924,0.0001182734,0.0003281769,0.00005964952,0.01044977],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002184354,"threshold_uncertainty_score":0.00730741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1028235888945273,"score_gpt":0.3587249372916659,"score_spread":0.2559013483971386,"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."}}