{"id":"W2021601835","doi":"10.1109/icc.2013.6655548","title":"Channel selection for heterogeneous nodes in cognitive networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Channel (broadcasting); Channel allocation schemes; Throughput; Computer network; Cognitive radio; Selection (genetic algorithm); Bandwidth (computing); Convergence (economics); Wireless ad hoc network; Reinforcement learning; Cognitive network; Distributed computing; Mathematical optimization; Wireless; Artificial intelligence; Telecommunications; Mathematics","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":[0.001214454,0.0005941339,0.0006386212,0.000511419,0.0007768621,0.000965238,0.001347945,0.0005318147,0.0006699124],"category_scores_gemma":[0.003392987,0.0002576259,0.0003050187,0.0004908637,0.001080306,0.0008800992,0.00110264,0.0005860441,0.0001490888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001103368,"about_ca_system_score_gemma":0.001147695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004641859,"about_ca_topic_score_gemma":0.004730699,"domain_scores_codex":[0.9993657,0.0001915892,0.0000177035,0.00009937279,0.0001723509,0.0001532076],"domain_scores_gemma":[0.9987495,0.0008134683,0.0001241908,0.00008811856,0.0001386776,0.00008597269],"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":[0.00006649671,0.000037048,0.0005320723,0.00002807106,0.00002341464,0.00007023137,0.00007845418,0.9287862,0.001715183,0.02440034,0.0007211005,0.04354149],"study_design_scores_gemma":[0.000008709581,0.00001395034,0.00006281739,0.00000213978,0.000004826885,0.00001263621,0.00001206584,0.9902291,0.0003720065,0.009020764,0.0002565489,0.000004339151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0318937,0.0003085244,0.9654135,0.0001367705,0.0000581699,0.00004298573,0.00001668962,0.000160649,0.00196897],"genre_scores_gemma":[0.919282,0.0002101277,0.07840624,0.00008456601,0.00005003256,0.00009148895,0.0000271,0.00002406884,0.001824322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004641859,"threshold_uncertainty_score":0.00922966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01799261393055212,"score_gpt":0.2445251221156911,"score_spread":0.226532508185139,"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."}}