{"id":"W2895792075","doi":"10.1002/cpe.5035","title":"Optimal matching between energy saving and traffic load for mobile multimedia communication","year":2018,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"National Natural Science Foundation of China","keywords":"Cellular network; Computer science; Operating expense; Cellular traffic; Maximization; Energy consumption; Computer network; Base station; Capital expenditure; Matching (statistics); Minification; Mobile telephony; Energy (signal processing); Efficient energy use; Telecommunications; Mobile radio; Engineering; Mathematical optimization","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":[0.0001386429,0.0001045023,0.000117802,0.00003379646,0.0002509743,0.00008263953,0.0000482488,0.00005057795,0.000002056141],"category_scores_gemma":[0.00008928964,0.0001145801,0.000008757698,0.00007445113,0.0001178018,0.0009519846,0.00002984868,0.00006220633,7.994545e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001950869,"about_ca_system_score_gemma":0.00001286234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000154112,"about_ca_topic_score_gemma":0.000004598486,"domain_scores_codex":[0.9994118,0.0000359555,0.0001985401,0.0001730575,0.00006384419,0.0001167611],"domain_scores_gemma":[0.999169,0.0004834279,0.00008148156,0.00007961985,0.0001291747,0.00005729058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003624652,0.00002588291,0.0001457523,0.0001319249,0.00003249515,7.090546e-7,0.07778286,0.111406,0.001139683,0.0006342575,0.00008862004,0.8085756],"study_design_scores_gemma":[0.0006389416,0.000144162,0.0001742076,0.00009285595,0.00003095413,0.00002431109,0.01041963,0.9819675,0.0005202651,0.0001615359,0.005569026,0.0002566156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3632356,0.00294139,0.6333653,0.00002277208,0.0001017101,0.0001455017,0.000003890777,0.00007817207,0.0001056764],"genre_scores_gemma":[0.9482226,0.0006945368,0.05085257,0.00002612003,0.00007242239,0.00009244982,0.00002069692,0.00001169223,0.000006897111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8705615,"threshold_uncertainty_score":0.4672439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649001071601078,"score_gpt":0.3060330158584909,"score_spread":0.2895430051424801,"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."}}