{"id":"W2909251905","doi":"10.1109/tcc.2019.2893228","title":"Computing-Aware Base Station Sleeping Mechanism in H-CRAN-Cloud-Edge Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Cloud Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cloud computing; Computer science; Base station; Server; Edge computing; Computer network; Mobile edge computing; Enhanced Data Rates for GSM Evolution; Distributed computing; Knapsack problem; Algorithm; Operating system; 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":[0.0009910511,0.001087043,0.001225499,0.0004228891,0.001006575,0.001452089,0.002637169,0.0008775026,0.001589351],"category_scores_gemma":[0.001459925,0.0004892003,0.0005671111,0.0009110525,0.0008721689,0.00122962,0.001197634,0.0008128808,0.0002011409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001473998,"about_ca_system_score_gemma":0.001562912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01108196,"about_ca_topic_score_gemma":0.01221352,"domain_scores_codex":[0.9992285,0.000165137,0.0000267885,0.0001938346,0.0001090434,0.0002767208],"domain_scores_gemma":[0.9993499,0.0002437302,0.0001087431,0.00006982935,0.0001271145,0.0001006324],"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":[0.0002617115,0.0000903618,0.0009226472,0.0001558337,0.00006384066,0.0002771013,0.0001075378,0.9397469,0.005745644,0.01620074,0.002384595,0.03404322],"study_design_scores_gemma":[0.000009981365,0.00004632102,0.0001329086,0.000005721919,0.00001519341,0.0000517758,0.00004110841,0.9964747,0.00071975,0.001994367,0.0005005977,0.000007556046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0712425,0.001099671,0.9219105,0.0003097024,0.0001351217,0.0001291449,0.0001030065,0.0002953477,0.004775035],"genre_scores_gemma":[0.926531,0.0004819045,0.07024264,0.0001387303,0.00006738104,0.00007023405,0.00009404379,0.0000526376,0.002321355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01108196,"threshold_uncertainty_score":0.02203494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01015362131334988,"score_gpt":0.2236305098929235,"score_spread":0.2134768885795737,"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."}}