{"id":"W3137135908","doi":"10.1109/mnet.011.2000644","title":"Customized Slicing for 6G: Enforcing Artificial Intelligence on Resource Management","year":2021,"lang":"en","type":"article","venue":"IEEE Network","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; Communication University of China; Fundamental Research Funds for the Central Universities; University of Science and Technology Beijing; National Natural Science Foundation of China","keywords":"Computer science; Resource management (computing); Resource allocation; Quality of service; Reinforcement learning; Slicing; Resource Management System; Resource (disambiguation); Service (business); Human resource management system; Distributed computing; Personalization; Computer network; Knowledge management; Artificial intelligence; Human resource management; World Wide Web","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":[0.001574327,0.0006710037,0.0005999022,0.0003381841,0.0005643818,0.001082085,0.001233961,0.0006958051,0.0009474949],"category_scores_gemma":[0.00340165,0.0002420807,0.0003830171,0.000404432,0.001388044,0.001706204,0.001386422,0.001578666,0.0001180388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001339758,"about_ca_system_score_gemma":0.001791034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005479742,"about_ca_topic_score_gemma":0.005846711,"domain_scores_codex":[0.9990779,0.000264796,0.00005216744,0.0001934682,0.0002416435,0.000169968],"domain_scores_gemma":[0.9985622,0.0005713775,0.0002426988,0.0002607533,0.0002151747,0.0001477762],"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":[0.0001260753,0.00008179557,0.001717183,0.00007858285,0.00005205389,0.0001648832,0.0001651303,0.8623588,0.01011113,0.04458966,0.00169274,0.07886208],"study_design_scores_gemma":[0.000005040841,0.00002896173,0.0001751463,0.000007199129,0.000006682317,0.00001972203,0.00001865813,0.9860134,0.001213656,0.01165879,0.0008457058,0.000007109722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04114297,0.0005209506,0.9526421,0.0006304903,0.00008152295,0.00007375458,0.00003878392,0.0005218865,0.00434756],"genre_scores_gemma":[0.9173598,0.0002994877,0.08117,0.0001763893,0.00004501216,0.00003792874,0.00003663964,0.00003568965,0.0008389677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005479742,"threshold_uncertainty_score":0.01089573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03494766369051894,"score_gpt":0.2681080634512222,"score_spread":0.2331603997607032,"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."}}