{"id":"W4313886758","doi":"10.23919/jcn.2022.000054","title":"Slicing-based resource optimization in multi-access edge network using ensemble learning aided DDPG algorithm","year":2023,"lang":"en","type":"article","venue":"Journal of Communications and Networks","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Division of Civil, Mechanical and Manufacturing Innovation; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Mobile edge computing; Wireless network; Distributed computing; Virtual network; Resource allocation; Wireless; Edge computing; Computer network; Server; Enhanced Data Rates for GSM Evolution; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001207501,0.00009680008,0.0002034261,0.0002484728,0.0003953261,0.0002615486,0.0009946097,0.00006946694,0.000001014505],"category_scores_gemma":[0.0000416009,0.00009412627,0.0000659471,0.0009961794,0.00004235752,0.0004175777,0.0004557374,0.0005510601,4.921581e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004524645,"about_ca_system_score_gemma":0.00006032084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006185498,"about_ca_topic_score_gemma":0.00003067412,"domain_scores_codex":[0.9987339,0.0003642811,0.0004282114,0.0001168607,0.0001409636,0.0002158243],"domain_scores_gemma":[0.9985106,0.0004345284,0.0003514951,0.0004964434,0.0001351584,0.00007176853],"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":[0.000008198445,0.00002958676,0.001887876,0.00000237683,0.00001051313,0.000006692811,0.0001296041,0.9625944,0.00001771613,0.00006534226,0.0001909603,0.03505677],"study_design_scores_gemma":[0.000534345,0.00004550776,0.001277092,0.0002197558,0.00001127921,0.00002368856,0.00006580645,0.9965685,0.000002343187,0.00004787075,0.001105242,0.00009857227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0109118,0.002455983,0.9856687,0.0006390153,0.0001405249,0.00006683441,2.040443e-7,0.00004818189,0.00006880198],"genre_scores_gemma":[0.8573427,0.001921447,0.1402747,0.0002656752,0.0001287006,0.000003087242,0.00000701821,0.00001397542,0.00004270832],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8464309,"threshold_uncertainty_score":0.3838358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07294231477596194,"score_gpt":0.31744040555547,"score_spread":0.2444980907795081,"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."}}