{"id":"W2084056659","doi":"10.1109/pimrc.2013.6666540","title":"Channel selection in Cognitive Radio Networks: A Switchable Bayesian Learning Automata approach","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Learning automata; Cognitive radio; Computer science; Channel (broadcasting); Probabilistic logic; Automaton; Bayesian probability; Bayesian network; Action selection; Selection (genetic algorithm); Theoretical computer science; Artificial intelligence; Computer network; Wireless; Telecommunications","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002428963,0.0001931727,0.000341037,0.0006540146,0.0002577867,0.0004643641,0.0005215717,0.0001413167,0.001960464],"category_scores_gemma":[0.002490845,0.0001449139,0.00006983145,0.00275838,0.00009958976,0.001298312,0.0002040363,0.0006476289,0.0006845334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001212542,"about_ca_system_score_gemma":0.00008009139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005165808,"about_ca_topic_score_gemma":0.00008959865,"domain_scores_codex":[0.9962761,0.0003893795,0.0005325455,0.0007723554,0.001289598,0.0007400154],"domain_scores_gemma":[0.9977188,0.001258194,0.0001384477,0.0002115959,0.0004765916,0.0001963563],"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":[0.0001082429,0.0004463514,0.01700941,0.00001075207,0.00004620464,0.00001923231,0.001524947,0.7336491,0.0001133352,0.0001593885,0.01254307,0.2343699],"study_design_scores_gemma":[0.0006831298,0.0001063579,0.01436628,0.00001539666,0.000001948252,0.00002172522,0.003053877,0.9767283,0.00009352536,0.004390779,0.0003499713,0.0001886785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007853938,0.00007769789,0.9787536,0.0002326848,0.00008469222,0.0007244923,7.695736e-7,0.0001684341,0.01210371],"genre_scores_gemma":[0.9746984,0.00002522645,0.005681543,0.00006576228,0.0001611782,0.0002281986,0.00000766993,0.00002557483,0.01910649],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9730721,"threshold_uncertainty_score":0.9989519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07435939515523153,"score_gpt":0.377254372149669,"score_spread":0.3028949769944375,"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."}}