{"id":"W2124050329","doi":"10.1109/tmc.2011.77","title":"Gossip-Enabled Stochastic Channel Negotiation for Cognitive Radio Ad Hoc Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Wireless ad hoc network; Cognitive radio; Computer network; Control channel; Channel (broadcasting); Vehicular ad hoc network; Negotiation; Overhead (engineering); Markov chain; Wireless network; Distributed computing; Gossip; Mobile ad hoc network; Markov process; Wireless; Telecommunications; Network packet; Telecommunications link","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":[0.0003756232,0.0003352176,0.0003583233,0.0002415804,0.0008320613,0.0001709978,0.0003578465,0.0001433795,0.00001666518],"category_scores_gemma":[0.00001244251,0.0003560536,0.0002501462,0.0006364331,0.00007401994,0.0003847802,0.000007505896,0.0004092841,0.00001750497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001254159,"about_ca_system_score_gemma":0.00006874991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001084242,"about_ca_topic_score_gemma":0.00002664175,"domain_scores_codex":[0.9977902,0.0001195542,0.0004290914,0.0007449083,0.0002216651,0.0006945778],"domain_scores_gemma":[0.9982935,0.0007759107,0.0002034181,0.0003352588,0.0002160638,0.0001759056],"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":[0.0001371215,0.0002720483,0.000001736222,0.00001701157,0.0001181569,0.00001209084,0.00308193,0.4783918,0.0000568089,0.0002686094,0.00003649989,0.5176062],"study_design_scores_gemma":[0.001231684,0.0006238665,0.0001517059,0.0002111142,0.00006899852,0.00004584976,0.0002084706,0.9952537,0.001272112,0.0004887304,0.00002760759,0.0004162017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01446735,0.0004709315,0.9813103,0.00003501727,0.001973498,0.001118613,0.000008809044,0.0004247438,0.0001907124],"genre_scores_gemma":[0.9880391,0.00005065363,0.01125041,0.0001903874,0.0002654991,0.00009236894,0.000005364855,0.00004393074,0.00006228993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9735717,"threshold_uncertainty_score":0.9998891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02396068588907999,"score_gpt":0.2412805858396167,"score_spread":0.2173198999505367,"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."}}