{"id":"W4401879240","doi":"10.1109/mnet.2024.3449288","title":"Enhanced DRL Strategy for Distributed Edge Computing in Vehicular Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Network","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Computer network; Edge computing; Distributed computing; Enhanced Data Rates for GSM Evolution; Vehicular ad hoc network; Wireless ad hoc network; Telecommunications; Wireless","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005414718,0.0004088104,0.0004688606,0.00008762412,0.0001051534,0.0002093622,0.000293852,0.0003384001,0.00001387279],"category_scores_gemma":[0.0000126549,0.0004412305,0.000205087,0.0009805083,0.00004406624,0.0001571351,0.00003776192,0.0006962006,0.00004331853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002389818,"about_ca_system_score_gemma":0.00004381962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008650797,"about_ca_topic_score_gemma":0.0001247677,"domain_scores_codex":[0.9974244,0.00006467953,0.0005634815,0.0005051708,0.0001885789,0.001253647],"domain_scores_gemma":[0.9990072,0.0003811355,0.00003793098,0.0003599172,0.0000449023,0.0001689557],"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":[0.00001928103,0.00001297666,0.00003558993,0.0001336875,0.0001020049,0.00007131341,0.0000474523,0.9421437,0.0001971324,0.000281164,0.04862079,0.008334889],"study_design_scores_gemma":[0.0004276971,0.00004502209,0.0003810581,0.0005295742,0.00004031892,0.00001217511,0.00001141382,0.9749673,0.0002488341,0.0006663086,0.02220156,0.0004687442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1197276,0.01395381,0.8492441,0.0000424858,0.01335112,0.0009686241,0.00003316623,0.001581343,0.00109784],"genre_scores_gemma":[0.988597,0.0001692113,0.0005417096,0.00005846893,0.01008732,0.00009326304,0.0002412146,0.0001537577,0.00005801248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8688695,"threshold_uncertainty_score":0.999804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01167059451315041,"score_gpt":0.2387186470333056,"score_spread":0.2270480525201551,"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."}}