{"id":"W4392940210","doi":"10.1109/tcomm.2024.3379368","title":"Distributed-Optimization With Centralized-Refining for Efficient Resource Allocation in Future Wireless Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canada Research Chairs","keywords":"Computer science; Resource allocation; Wireless; Distributed computing; Refining (metallurgy); Wireless network; Computer network; Resource management (computing); Telecommunications","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":[0.001444974,0.0008177111,0.0007709409,0.0003591265,0.0004665664,0.0006901704,0.0009713175,0.0005161404,0.001003821],"category_scores_gemma":[0.001718156,0.0003040406,0.0005801169,0.0006859726,0.0007932081,0.0009050632,0.0007749821,0.0008457022,0.0001247195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009117041,"about_ca_system_score_gemma":0.001420117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004633475,"about_ca_topic_score_gemma":0.004744286,"domain_scores_codex":[0.999388,0.0002484826,0.00001850435,0.0001091687,0.0001631836,0.00007267463],"domain_scores_gemma":[0.9995271,0.0002267919,0.00006967408,0.0000711057,0.00008416861,0.00002116861],"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":[0.00002266585,0.0000264415,0.0001815577,0.00002335897,0.00001595633,0.00002117605,0.00001553509,0.9709646,0.001112976,0.01256318,0.000437964,0.01461456],"study_design_scores_gemma":[0.000005930011,0.00001427353,0.00004888677,0.000001549135,0.000003024677,0.000005980945,0.000004327389,0.9953511,0.0002718631,0.004070679,0.0002197034,0.000002618299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008705756,0.0001509647,0.989342,0.00008970263,0.0000143742,0.00002872279,0.000009454614,0.00009863206,0.001560453],"genre_scores_gemma":[0.7937927,0.0003239907,0.2035857,0.00009723876,0.00003224152,0.0001243207,0.00004783392,0.00006755127,0.001928376],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004633475,"threshold_uncertainty_score":0.00921303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01028436978096278,"score_gpt":0.2326594619359557,"score_spread":0.2223750921549929,"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."}}