{"id":"W3169712129","doi":"10.1109/tccn.2021.3085769","title":"Cooperative Sensing With Heterogeneous Spectrum Availability in Cognitive Radio","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Cognitive radio; Computer science; Overhead (engineering); Markov process; Markov chain; Reliability (semiconductor); Stochastic geometry; Distributed computing; Computer network; Shadow mapping; Fuse (electrical); Markov model; Telecommunications; Machine learning; Wireless; Artificial intelligence","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":[0.001998767,0.0006026597,0.0007828847,0.0005877936,0.0005136357,0.0007223822,0.001324001,0.0007374032,0.0004057296],"category_scores_gemma":[0.004639405,0.0004577808,0.0007022159,0.0005540443,0.001448256,0.001258544,0.001485036,0.0006301122,0.00009466985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008577589,"about_ca_system_score_gemma":0.001023144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003730228,"about_ca_topic_score_gemma":0.002727492,"domain_scores_codex":[0.9988971,0.0003829722,0.00003785113,0.0002900616,0.0002590254,0.0001329907],"domain_scores_gemma":[0.996572,0.002568831,0.0003291516,0.0002464258,0.0001842609,0.00009930682],"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":[0.0001277718,0.00006846197,0.0009447965,0.00006129253,0.00006572506,0.0001987295,0.0001958214,0.9305406,0.005861492,0.02994695,0.0004663063,0.0315221],"study_design_scores_gemma":[0.000006909896,0.00002498486,0.0001337137,0.000002955719,0.000007812871,0.00002729377,0.00001505448,0.9914357,0.0005899991,0.007638608,0.0001110304,0.000005949226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03937189,0.0002415158,0.9587495,0.0001029073,0.00002038688,0.00002902945,0.00001238389,0.0001287061,0.001343676],"genre_scores_gemma":[0.9588271,0.0001188537,0.04038613,0.00006187942,0.00002429645,0.00005266419,0.0000160153,0.00001305735,0.0004999401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003730228,"threshold_uncertainty_score":0.01057065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03424303304759925,"score_gpt":0.2685219836211188,"score_spread":0.2342789505735196,"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."}}