{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007945491,0.0001502256,0.0002071102,0.0002608664,0.0005282484,0.0003912645,0.002350721,0.00003244092,0.000006012157],"category_scores_gemma":[0.00004028224,0.000167951,0.00006209045,0.001112077,0.00002450708,0.0006450544,0.001658024,0.0003820353,0.00002171998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001384887,"about_ca_system_score_gemma":0.00008155044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001583011,"about_ca_topic_score_gemma":0.000004491078,"domain_scores_codex":[0.998161,0.0001757985,0.0003326869,0.0004757494,0.0003389369,0.000515855],"domain_scores_gemma":[0.9991218,0.0002148898,0.0001298754,0.0004130919,0.00004881998,0.00007152028],"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":[0.00003012222,0.000500262,0.1632806,0.0001284063,0.00006377114,0.0006134033,0.01352546,0.04046537,0.008727885,0.002005116,0.1104027,0.6602568],"study_design_scores_gemma":[0.001032432,0.00007771113,0.04259275,0.00007200121,0.000006261663,0.00005037319,0.00009085265,0.9137014,0.004609157,0.002946303,0.03409327,0.000727553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8979586,0.000153028,0.08140354,0.000356726,0.01768816,0.0001565917,3.264445e-7,0.0002631379,0.002019848],"genre_scores_gemma":[0.996726,0.000001680597,0.001351125,0.0006306032,0.001198981,0.00001065993,0.00000174939,0.0000140034,0.00006520164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8732359,"threshold_uncertainty_score":0.6848841,"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."}}