{"id":"W4402834104","doi":"10.1109/vtc2024-spring62846.2024.10683050","title":"Prioritized Task Offloading in Vehicular Edge Computing Using Deep Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Reinforcement learning; Computer science; Task (project management); Edge computing; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Human–computer interaction; Engineering","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":[0.0009525569,0.0006396997,0.0008118945,0.0002805558,0.0004566323,0.000738777,0.001343199,0.0005822096,0.001404145],"category_scores_gemma":[0.002689559,0.0002710954,0.0002380891,0.0003599689,0.0006883299,0.001073647,0.001057577,0.0009751135,0.0001762399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009470779,"about_ca_system_score_gemma":0.002050713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005906935,"about_ca_topic_score_gemma":0.008880538,"domain_scores_codex":[0.9994944,0.0001297843,0.00001777445,0.0001053407,0.00007953902,0.0001730577],"domain_scores_gemma":[0.9991042,0.0004533932,0.00008424829,0.00007206159,0.0001465735,0.0001395158],"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":[0.0002671312,0.0001488344,0.001381469,0.00006278313,0.00002335574,0.00008143065,0.00007127639,0.9280412,0.002797375,0.007923503,0.001570301,0.05763131],"study_design_scores_gemma":[0.000006864397,0.0000247261,0.00007308894,0.000002223073,0.000002664929,0.000006756247,0.00001105108,0.9966152,0.0003057105,0.002778759,0.0001704402,0.000002541366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1245827,0.000597143,0.869136,0.0005278221,0.000124808,0.00008742967,0.00006475559,0.0004878678,0.004391472],"genre_scores_gemma":[0.9785702,0.00009760357,0.01967252,0.0001095113,0.00001779148,0.00003544369,0.00003557535,0.00002481053,0.001436438],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005906935,"threshold_uncertainty_score":0.0117451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02988102745231301,"score_gpt":0.289332701834236,"score_spread":0.259451674381923,"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."}}