{"id":"W2071858447","doi":"10.1109/pimrc.2011.6139942","title":"Distributed self-optimization for efficient reconfiguration in overlapping heterogenous wireless access networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Control reconfiguration; Computer science; Distributed computing; Heterogeneous network; Wireless network; Distributed algorithm; Bandwidth (computing); Optimization problem; Wireless; Computer network; Algorithm; Embedded system","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":[0.0005671335,0.0004889032,0.0006173169,0.0003944731,0.000349915,0.0004510616,0.0007586845,0.0004199364,0.0007143503],"category_scores_gemma":[0.0008437985,0.0002809809,0.0003292302,0.0003223839,0.0005107399,0.0005313595,0.0006386638,0.0003825728,0.00008917067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004568904,"about_ca_system_score_gemma":0.0005333453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001963445,"about_ca_topic_score_gemma":0.001709274,"domain_scores_codex":[0.9997721,0.00008014966,0.000008962456,0.00004160934,0.00005751,0.00003963857],"domain_scores_gemma":[0.9996555,0.000180396,0.00006333843,0.00002582143,0.00005094834,0.00002385391],"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":[0.00003185618,0.00002376516,0.0003040271,0.00001283196,0.00002337415,0.0000268641,0.00002930294,0.9749212,0.001431973,0.005358643,0.0002448213,0.01759136],"study_design_scores_gemma":[0.000005573776,0.00000952824,0.00003104027,5.905821e-7,0.000001674076,0.000005093917,0.000002630308,0.9990222,0.0001267854,0.000694412,0.00009937966,9.434613e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03308771,0.00009333712,0.9654071,0.00006279638,0.00001243179,0.00002003141,0.0000074502,0.0001285381,0.001180519],"genre_scores_gemma":[0.8635436,0.00009485309,0.1343576,0.00005282451,0.00001500733,0.0001330181,0.00003208525,0.00003688382,0.001734144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001963445,"threshold_uncertainty_score":0.003904045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01854145433993236,"score_gpt":0.2213719457030472,"score_spread":0.2028304913631148,"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."}}