{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004648903,0.000369756,0.0003888517,0.0005240975,0.0002222684,0.0006964311,0.0004046234,0.0005544279,0.002172881],"category_scores_gemma":[0.002284678,0.0001965669,0.0001988048,0.0005254724,0.0003960572,0.0006336283,0.0005413868,0.00027439,0.0002210913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008841529,"about_ca_system_score_gemma":0.0005179247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001539939,"about_ca_topic_score_gemma":0.001433819,"domain_scores_codex":[0.9998264,0.00005287691,0.000004642939,0.00002928026,0.0000351575,0.00005173434],"domain_scores_gemma":[0.9994703,0.0003568014,0.00004731999,0.00002765992,0.00006835909,0.00002957675],"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":[0.0001208554,0.00009813876,0.0009402737,0.00003530729,0.0000213884,0.00003393572,0.00002351945,0.9568182,0.005208871,0.01345499,0.0009294136,0.02231519],"study_design_scores_gemma":[0.000004727291,0.00002298822,0.000338021,0.000002496801,0.000004799006,0.000008380405,0.00001828232,0.9938517,0.0009552691,0.004606266,0.0001839913,0.000003096908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5225862,0.0005036896,0.4497167,0.0007090182,0.00005299568,0.00009361196,0.0001573461,0.0001922533,0.02598819],"genre_scores_gemma":[0.9904241,0.00006445573,0.008050258,0.00003285525,0.00001133637,0.00002046617,0.00002352781,0.00001727499,0.001355609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002172881,"threshold_uncertainty_score":0.007269025,"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."}}