{"id":"W3164836019","doi":"10.1109/tnsm.2021.3083073","title":"Data-Driven Energy Conservation in Cellular Networks: A Systems Approach","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Academy of Finland","keywords":"Computer science; Energy consumption; Provisioning; Key (lock); Overhead (engineering); Context (archaeology); Energy (signal processing); Efficient energy use; Cellular network; Energy conservation; Base station; Distributed computing; Real-time computing; Computer network; Computer security","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":[0.0006467643,0.0006895074,0.0005842126,0.000762941,0.0004591989,0.001712621,0.001177906,0.0009958844,0.001077349],"category_scores_gemma":[0.002303146,0.0004070222,0.0005476457,0.001190408,0.000764335,0.001651898,0.0009224711,0.0009348727,0.0001886267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001829795,"about_ca_system_score_gemma":0.0008957914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007887401,"about_ca_topic_score_gemma":0.005233842,"domain_scores_codex":[0.9996321,0.0001117715,0.0000182084,0.00008130672,0.0001188908,0.00003766228],"domain_scores_gemma":[0.9991158,0.0005268771,0.00007824697,0.00007159831,0.0001747762,0.00003273171],"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":[0.00001667048,0.00002800019,0.0007592918,0.00005036397,0.00002757795,0.00005686857,0.00004902616,0.9597868,0.0008422526,0.02599732,0.0005123744,0.01187346],"study_design_scores_gemma":[0.000001779786,0.00001164457,0.0001557556,0.000007211371,0.00000557327,0.00001219637,0.0000134281,0.9885872,0.0002467253,0.01025654,0.000696811,0.000005147393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02709159,0.002109906,0.9629182,0.001433431,0.0001363988,0.00007704269,0.0002195108,0.0002157627,0.005798292],"genre_scores_gemma":[0.9262817,0.002208358,0.06677415,0.0002434927,0.0002767598,0.0001472543,0.0001849586,0.00009034713,0.003793115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007887401,"threshold_uncertainty_score":0.015683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01693018656250205,"score_gpt":0.1986742223997235,"score_spread":0.1817440358372215,"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."}}