{"id":"W3139045596","doi":"10.3390/s21062076","title":"Optimal Access Point Power Management for Green IEEE 802.11 Networks","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Mathematical optimization; Key (lock); Nonlinear programming; Software deployment; Decomposition; Integer programming; Transmission (telecommunications); Power (physics); Point (geometry); Nonlinear system; Distributed computing; Computer network; Algorithm; Telecommunications; Mathematics","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":[0.0008291439,0.0007872345,0.0007286012,0.0005912909,0.0004397172,0.0009199445,0.0008511267,0.0007477981,0.002019508],"category_scores_gemma":[0.001148235,0.0004119323,0.0003396278,0.0006890387,0.0007552732,0.001128955,0.0007726387,0.0007760696,0.0002584915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001188019,"about_ca_system_score_gemma":0.0009621639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002728862,"about_ca_topic_score_gemma":0.004359252,"domain_scores_codex":[0.9996006,0.0001275135,0.00001225308,0.00007480687,0.0001060968,0.00007876616],"domain_scores_gemma":[0.9996849,0.0001841593,0.00004079612,0.00002200674,0.0000461541,0.00002203692],"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":[0.00005447751,0.00005309044,0.0002349938,0.00004007461,0.00001956462,0.00003838785,0.00005669446,0.918238,0.002433146,0.01328963,0.001058823,0.06448314],"study_design_scores_gemma":[0.000009462174,0.00002196265,0.00008424677,0.000003973704,0.00000523229,0.00001207424,0.00001556981,0.9896459,0.0006389047,0.008878559,0.0006802384,0.00000395665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01424253,0.0002830658,0.9828015,0.0001311561,0.00002547942,0.00003244757,0.00001822179,0.0002017098,0.002263847],"genre_scores_gemma":[0.6901238,0.0004598511,0.3034045,0.0001307204,0.00004421153,0.0001227885,0.00007161759,0.00008922955,0.005553315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002728862,"threshold_uncertainty_score":0.008619785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02285820381752215,"score_gpt":0.2862406702470449,"score_spread":0.2633824664295228,"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."}}