{"id":"W2145133983","doi":"10.1109/ccnc.2009.4784696","title":"Autonomic Management for Capacity Improvement in Wireless Networks","year":2009,"lang":"en","type":"preprint","venue":"","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer network; Computer science; Blocking (statistics); Radio resource management; Wireless network; Call blocking; Load balancing (electrical power); Resource management (computing); Wireless; Local area network; Voice over IP; Distributed computing; Telecommunications; Quality of service; Geography; The Internet","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008356317,0.0004242417,0.0005295664,0.000173759,0.00008242489,0.0004368731,0.00203749,0.0003550761,0.00001067087],"category_scores_gemma":[0.0000015295,0.0004061162,0.0002197503,0.0001798326,0.00002555192,0.0001532599,0.001706792,0.0005521036,0.000006024108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003477189,"about_ca_system_score_gemma":0.00007930344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001356387,"about_ca_topic_score_gemma":0.0001378591,"domain_scores_codex":[0.997264,0.0000611686,0.0006519822,0.001114749,0.0002095014,0.0006986475],"domain_scores_gemma":[0.9982062,0.00006163168,0.0002604276,0.001302328,0.00005091451,0.0001185229],"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":[0.00002133543,0.000167713,0.00008196221,0.000219597,0.00005648108,0.00001045036,0.00006663279,0.1774539,0.000006237441,0.2504807,0.004013393,0.5674216],"study_design_scores_gemma":[0.0007421609,0.0001226458,0.00145744,0.0002831472,0.000009069516,6.918601e-7,0.000003772587,0.9461001,0.000109657,0.04669977,0.003937339,0.0005341791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002412968,0.00004214801,0.9725383,0.0006775283,0.0008208753,0.01862466,0.000004519317,0.0002437119,0.004635327],"genre_scores_gemma":[0.6815867,0.0001990296,0.2696488,0.002354058,0.0007561748,0.04405317,0.00004776928,0.00005626292,0.001298043],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7686462,"threshold_uncertainty_score":0.9998391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02323094347609201,"score_gpt":0.2597360388132219,"score_spread":0.2365050953371299,"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."}}