{"id":"W2962576838","doi":"10.1109/icc.2019.8761840","title":"Cutting Down Idle Listening Time: A NDN-Enabled Power Saving Mode Design for WLAN","year":2019,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Idle; Wireless; Power consumption; Computer network; Real-time computing; State (computer science); Power (physics); Transmission (telecommunications); Energy consumption; Channel (broadcasting); Embedded system; Throughput; Operating system; Telecommunications; Electrical engineering","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":[0.0003300504,0.0004287466,0.0003561102,0.0004118673,0.0003410922,0.0006114787,0.001780638,0.000275825,0.001436302],"category_scores_gemma":[0.000458046,0.0002161132,0.0002551385,0.0002424933,0.0002809964,0.000852546,0.0006166823,0.000382001,0.0003408316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008272558,"about_ca_system_score_gemma":0.0005616979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001949157,"about_ca_topic_score_gemma":0.00346801,"domain_scores_codex":[0.9997342,0.00004569557,0.00002394716,0.0000637531,0.00007600283,0.00005627351],"domain_scores_gemma":[0.9997241,0.00003998089,0.0000453713,0.0000314131,0.0001234016,0.00003574763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001048726,0.0004164025,0.007232217,0.0005761049,0.0001490523,0.0005995092,0.0006871585,0.05689057,0.4959432,0.0152858,0.008308337,0.4128629],"study_design_scores_gemma":[0.0001550885,0.001044674,0.002824145,0.00007720117,0.0001804768,0.000730894,0.0001845456,0.8344693,0.1351371,0.003025754,0.0220743,0.00009651233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.168651,0.002321551,0.814564,0.0006980676,0.0002398689,0.0001987093,0.0001540085,0.002341307,0.01083144],"genre_scores_gemma":[0.9323332,0.0004168834,0.06406916,0.0002501538,0.00005801643,0.0001076247,0.00007459624,0.00006400868,0.002626379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001949157,"threshold_uncertainty_score":0.006002188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01481367529322646,"score_gpt":0.2308157450450223,"score_spread":0.2160020697517958,"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."}}