{"id":"W2147758080","doi":"10.1109/wcl.2014.022314.130796","title":"Dynamic Spectrum Management in Multi-Radio Access Technology (RAT) Cellular Systems","year":2014,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Spectrum management; Frequency allocation; Radio resource management; Computer network; Cognitive radio; Spectrum (functional analysis); Access technology; Resource management (computing); Distributed computing; Telecommunications; Wireless; Wireless network","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":[0.0001764209,0.0002647522,0.0003131491,0.0006332014,0.000150589,0.00008986013,0.001838919,0.0001395454,0.000002732885],"category_scores_gemma":[0.000004843658,0.0003231359,0.0000453722,0.001064313,0.000165315,0.0003088134,0.0002255162,0.0004605008,0.00004226477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003888719,"about_ca_system_score_gemma":0.000005662599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002367891,"about_ca_topic_score_gemma":0.0001817886,"domain_scores_codex":[0.9985374,0.0001224958,0.0004729521,0.0002860029,0.000145271,0.0004358661],"domain_scores_gemma":[0.9975493,0.00008484043,0.0001001305,0.002187102,0.00002352508,0.00005509738],"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":[0.000002157796,0.00004880474,0.0004790112,0.00007026798,0.00004047037,0.000005316179,0.0000554715,0.9882224,0.005964607,0.001956466,0.0001900762,0.002964963],"study_design_scores_gemma":[0.0005374366,0.000005521659,0.0003424489,0.0001295556,0.00001699212,0.000004613179,0.00006065492,0.9956766,0.0008329335,0.0000625044,0.001998107,0.0003326736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1189367,0.0007638012,0.8760788,0.001755624,0.0006278084,0.0006067549,0.000003811247,0.000829931,0.0003968284],"genre_scores_gemma":[0.9768618,0.00142563,0.0210168,0.00009587243,0.00002264633,0.0003829317,0.00006002718,0.00009359339,0.00004067156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8579252,"threshold_uncertainty_score":0.9999221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01392078729543418,"score_gpt":0.246635664020538,"score_spread":0.2327148767251039,"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."}}