{"id":"W7071617608","doi":"","title":"A Trustworthy Deep Reinforcement Learning Framework for Slicing in Next-Generation Open Radio Access Networks","year":2025,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Software deployment; Slicing; Radio access network; Network architecture; Key (lock); Reuse","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002155422,0.0008308795,0.001119265,0.0003722248,0.0003797359,0.0009778361,0.001591682,0.001071884,0.001792641],"category_scores_gemma":[0.004862782,0.0005032111,0.0005783149,0.000311907,0.001616264,0.001169764,0.001533187,0.00231478,0.0002310255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001788354,"about_ca_system_score_gemma":0.002219527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009280764,"about_ca_topic_score_gemma":0.008092681,"domain_scores_codex":[0.9992156,0.0002838005,0.00004116131,0.0001624458,0.000163239,0.0001337651],"domain_scores_gemma":[0.9979439,0.001266329,0.0002284993,0.0001061173,0.0003066859,0.0001483773],"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":[0.00003336136,0.00001886623,0.0003128161,0.00002816704,0.00001794553,0.00003755023,0.00003856839,0.9781754,0.000431551,0.009534017,0.0002626034,0.01110923],"study_design_scores_gemma":[0.000003268779,0.000009381035,0.00001957373,0.000003117788,0.000002263254,0.000002366335,0.000002325149,0.9968768,0.00007315359,0.002914935,0.00009112885,0.000001627743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01372657,0.0003115167,0.9836209,0.0003160809,0.00003088012,0.00003317127,0.00003068475,0.0002977819,0.001632336],"genre_scores_gemma":[0.8901615,0.0003273286,0.1059066,0.0002214157,0.00004753298,0.0001485185,0.00008343659,0.00007760964,0.003026069],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009280764,"threshold_uncertainty_score":0.01845348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02371036507109542,"score_gpt":0.2458987760061925,"score_spread":0.2221884109350971,"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."}}