{"id":"W2081804334","doi":"10.1109/glocom.2012.6503147","title":"Rank-optimal channel selection strategy in cognitive networks","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Quality of service; Channel (broadcasting); Computer science; Selection (genetic algorithm); Cognitive radio; Throughput; Rank (graph theory); Convergence (economics); Quality (philosophy); Provisioning; Computer network; Mathematical optimization; Artificial intelligence; Mathematics; Wireless; Telecommunications; Combinatorics","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":[0.002579768,0.0001238755,0.0002026693,0.0003662935,0.0001063147,0.0001226173,0.0002679164,0.00010783,0.002083173],"category_scores_gemma":[0.001098935,0.00009009422,0.00005223259,0.001593841,0.00008875641,0.001080186,0.00009749197,0.000340717,0.0005253137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006325818,"about_ca_system_score_gemma":0.00004808159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006484527,"about_ca_topic_score_gemma":0.0001350037,"domain_scores_codex":[0.9973881,0.0002183466,0.0003734608,0.0003375352,0.0009849626,0.000697581],"domain_scores_gemma":[0.9980441,0.001232447,0.00007317798,0.0001436701,0.0003211002,0.0001855413],"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":[0.0004956782,0.0005410385,0.04671182,0.000002699256,0.00003319161,0.00001957338,0.0009089704,0.6223544,0.00007576853,0.0006025417,0.007443023,0.3208113],"study_design_scores_gemma":[0.001237958,0.0001851441,0.08895136,0.00001210979,0.000004079639,0.00002714298,0.003508114,0.9009246,0.001016321,0.003071667,0.000774388,0.0002871854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07771713,0.0002549122,0.9095588,0.0001062457,0.0002546877,0.0003189791,0.000003423416,0.00005221302,0.01173365],"genre_scores_gemma":[0.9932246,0.00001664992,0.0004901221,0.00006503512,0.0003610691,0.00003056428,0.000003734612,0.00001083488,0.005797412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9155074,"threshold_uncertainty_score":0.9988291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1653851607582504,"score_gpt":0.4570105232673575,"score_spread":0.2916253625091071,"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."}}