{"id":"W4401328662","doi":"10.1109/tnsm.2024.3438438","title":"5G Service Function Chain Provisioning: A Deep Reinforcement Learning-Based Framework","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal; Concordia University; École de Technologie Supérieure","funders":"Mitacs","keywords":"Computer science; Reinforcement learning; Provisioning; Computer network; Function (biology); Chain (unit); Distributed computing; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009735581,0.000873351,0.0008720733,0.0003401029,0.0002953421,0.0007768151,0.001318435,0.001123463,0.002278463],"category_scores_gemma":[0.001579294,0.0003290721,0.0004102699,0.0003780426,0.0007576568,0.000831449,0.0008745643,0.001456491,0.0002196799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00166689,"about_ca_system_score_gemma":0.001813101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01495204,"about_ca_topic_score_gemma":0.0128866,"domain_scores_codex":[0.9996309,0.0001061908,0.00001051377,0.00006681852,0.00007847748,0.0001070869],"domain_scores_gemma":[0.9994936,0.0002439274,0.00006160525,0.00002493989,0.0001158836,0.00005995402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002630157,0.0000220131,0.0003027557,0.00001724221,0.0000115629,0.00003154868,0.0000126655,0.9861196,0.0003370319,0.00364252,0.0003936631,0.009083119],"study_design_scores_gemma":[0.000001430632,0.00000419544,0.00001387254,0.000001015876,0.000001088963,0.000001846591,0.000001231199,0.9992749,0.00004210565,0.0005994808,0.00005804178,7.331271e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03335825,0.0005640376,0.9604891,0.0005408369,0.00006879797,0.000049528,0.00007938515,0.0003901085,0.004460063],"genre_scores_gemma":[0.9352823,0.0002489953,0.06086915,0.0002136024,0.00004855249,0.00008491649,0.0001073686,0.00005082268,0.003094319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01495204,"threshold_uncertainty_score":0.02973002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009964577736315216,"score_gpt":0.2066566030512659,"score_spread":0.1966920253149507,"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."}}