{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003524268,0.0003332234,0.0004048316,0.0002059675,0.0009365135,0.0003197949,0.0002940325,0.0001051596,0.00002135826],"category_scores_gemma":[0.00001441772,0.0003357216,0.00009375777,0.001317449,0.0004044499,0.0003622377,0.0000351167,0.0007936851,0.000008209758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001247287,"about_ca_system_score_gemma":0.0001835843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006067298,"about_ca_topic_score_gemma":0.003826711,"domain_scores_codex":[0.9974346,0.0006692348,0.0003980414,0.0007720977,0.0002240097,0.0005020743],"domain_scores_gemma":[0.9966175,0.001980017,0.0001210132,0.0007436289,0.0003885708,0.000149267],"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":[0.0002353393,0.0007058796,0.0006654574,0.00002051666,0.0003355943,0.0004165008,0.002863683,0.001754283,0.0002324082,0.0005022845,0.000006339864,0.9922617],"study_design_scores_gemma":[0.00533309,0.0009946896,0.001944629,0.00394398,0.0002825794,0.002006468,0.002450387,0.9589887,0.02007348,0.001521563,0.0007044889,0.001755929],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05564466,0.002770677,0.9376609,0.0008341294,0.00020813,0.0004211104,0.00001930574,0.0001268926,0.002314148],"genre_scores_gemma":[0.9899734,0.004343962,0.004928391,0.000556408,0.00006745605,0.00002543405,0.00001405423,0.00002943228,0.00006148576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9905058,"threshold_uncertainty_score":0.9999095,"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."}}