{"id":"W2139803769","doi":"10.1002/ett.2527","title":"Energy‐efficient exploration and exploitation of multichannel diversity in spectrum sharing systems","year":2012,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Overhead (engineering); Cognitive radio; Computer science; Channel (broadcasting); Diversity gain; Throughput; Diversity (politics); Energy (signal processing); Energy consumption; Power (physics); Distributed computing; Telecommunications; Engineering; Wireless; Mathematics; Electrical engineering; MIMO","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":[],"consensus_categories":[],"category_scores_codex":[0.0002928621,0.0001203202,0.0001596415,0.0006160107,0.0004440215,0.00004130325,0.0005214501,0.00007766947,0.000001295589],"category_scores_gemma":[0.00002157612,0.0001292497,0.00003702797,0.0008558602,0.00009120454,0.0005814364,0.0001332354,0.000211469,0.000001353088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009737252,"about_ca_system_score_gemma":0.000008870605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002772826,"about_ca_topic_score_gemma":0.0001323588,"domain_scores_codex":[0.9990802,0.00006114974,0.0002664367,0.0002080206,0.0001360345,0.0002482006],"domain_scores_gemma":[0.9989658,0.0001666714,0.000113949,0.0006805389,0.00004661222,0.00002641354],"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":[0.0000256476,0.001230729,0.003180882,0.00005739088,0.00009619266,0.000001795146,0.01451781,0.3258796,0.001226081,0.2870817,0.00001509366,0.366687],"study_design_scores_gemma":[0.0002719024,0.00005407511,0.001530454,0.0001151663,0.00001459722,0.000006965434,0.005997815,0.9828182,0.005068952,0.003848243,0.00006512566,0.000208433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08474265,0.0009874768,0.9117848,0.001589225,0.0001181973,0.0001385736,0.000001596439,0.0004322766,0.0002052469],"genre_scores_gemma":[0.9856067,0.001376845,0.01294816,0.000006901202,0.000005253529,0.00003505138,0.000001855255,0.00000735401,0.00001188619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9008641,"threshold_uncertainty_score":0.527065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04247931937506697,"score_gpt":0.2571487704345232,"score_spread":0.2146694510594562,"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."}}