{"id":"W4226481759","doi":"10.1109/access.2022.3167641","title":"Parked Vehicles Task Offloading in Edge Computing","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Carleton University","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; University of Calgary; National Science Foundation","keywords":"Computer science; Heuristics; Edge computing; Computation offloading; Distributed computing; Leverage (statistics); Task (project management); Orchestration; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Operating system","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":[0.0004311261,0.0006542348,0.0006983165,0.0003401533,0.0009133065,0.000939464,0.001253145,0.0005726393,0.002043963],"category_scores_gemma":[0.0009380874,0.0002132128,0.0004226273,0.0005560974,0.000421063,0.001011898,0.001057554,0.0004947194,0.0002500697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006564932,"about_ca_system_score_gemma":0.001053475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006085114,"about_ca_topic_score_gemma":0.008120095,"domain_scores_codex":[0.9995547,0.00007934161,0.00001723313,0.0001063065,0.00006105285,0.0001813429],"domain_scores_gemma":[0.9996459,0.000118829,0.00003359781,0.00007301334,0.00004985358,0.00007876485],"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":[0.0003636312,0.0002165591,0.001838971,0.0001247293,0.0000421397,0.0003210023,0.000209743,0.8850237,0.007039827,0.0200037,0.00385465,0.08096141],"study_design_scores_gemma":[0.00001181358,0.00003298876,0.0002285558,0.000004385453,0.000007551386,0.00003973383,0.0000666214,0.99204,0.001146676,0.005392203,0.00102435,0.000005029257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2765988,0.0007463559,0.7071494,0.0004565679,0.0001754379,0.0001853064,0.0001534287,0.0008420401,0.01369249],"genre_scores_gemma":[0.9286727,0.0001225365,0.0689339,0.00006967825,0.00002252245,0.00005434362,0.00009011748,0.000057188,0.001977005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006085114,"threshold_uncertainty_score":0.01209944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03431295459016268,"score_gpt":0.2933271060104535,"score_spread":0.2590141514202908,"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."}}