{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006370129,0.000213887,0.0002834871,0.00006499877,0.0003551709,0.0002600673,0.000634,0.00009401734,0.00001116016],"category_scores_gemma":[0.0000433382,0.0002114111,0.0001670483,0.000765449,0.00002596702,0.000122425,0.0001882706,0.0001982329,0.0000549071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006815088,"about_ca_system_score_gemma":0.00003599734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005330342,"about_ca_topic_score_gemma":0.00001276726,"domain_scores_codex":[0.9979233,0.00008620311,0.0004071639,0.000631974,0.00029564,0.0006557867],"domain_scores_gemma":[0.9981964,0.0007447864,0.0001220524,0.0007488219,0.00007449934,0.0001134196],"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":[0.0001614875,0.00006061087,0.00003121667,0.00002926113,0.00006376936,0.00008266082,0.0002674734,0.4925988,0.00002411842,0.1411646,0.06802335,0.2974927],"study_design_scores_gemma":[0.0008955114,0.000272356,0.0001219149,0.0007311869,0.0000807824,0.00003170593,0.0002084852,0.4965316,0.005278157,0.1540004,0.3407806,0.001067289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001312305,0.0002726224,0.9846693,0.0006504923,0.004782024,0.0003558134,0.00000120194,0.0003261271,0.007630088],"genre_scores_gemma":[0.6330365,0.00020616,0.3308659,0.01176404,0.01957465,0.0003868997,0.00003304134,0.0001319493,0.004000857],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6538034,"threshold_uncertainty_score":0.8621094,"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."}}