{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007174826,0.0003999046,0.0004184417,0.0004187761,0.000626573,0.0009488496,0.000671729,0.0004263279,0.001433265],"category_scores_gemma":[0.001835228,0.0001536249,0.0002143935,0.0005097868,0.0005597638,0.001221093,0.0008142301,0.0007861089,0.0002262494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004154973,"about_ca_system_score_gemma":0.0003312429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004010369,"about_ca_topic_score_gemma":0.0004248856,"domain_scores_codex":[0.9996612,0.00008292627,0.00001640049,0.00005117859,0.0001373991,0.00005089328],"domain_scores_gemma":[0.9994922,0.0002393971,0.00005956503,0.00008449492,0.00008190614,0.00004246856],"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":[0.0001648868,0.0001624756,0.001633734,0.0001771927,0.00009377092,0.0002510656,0.0001797899,0.3990343,0.0261887,0.1218316,0.00775462,0.4425279],"study_design_scores_gemma":[0.00003354811,0.0001111285,0.0006608817,0.00002601769,0.00003243804,0.0002029571,0.00004173636,0.9045594,0.006395756,0.0748621,0.01304688,0.00002710002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06469055,0.00536583,0.9056174,0.001257677,0.0005076089,0.0000734322,0.00003666566,0.001585841,0.02086496],"genre_scores_gemma":[0.9335768,0.001236489,0.06198461,0.0001913116,0.0003650443,0.00006788476,0.00004452381,0.0001030384,0.002430285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001433265,"threshold_uncertainty_score":0.004794717,"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."}}