{"id":"W2007538147","doi":"10.1109/lwc.2015.2422820","title":"Transient Analysis for a Trust-Based Cognitive Radio Collaborative Spectrum Sensing Scheme","year":2015,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Transient (computer programming); Cognitive radio; Computer science; Cascading Style Sheets; Scheme (mathematics); Transient analysis; Process (computing); Energy (signal processing); Topology (electrical circuits); Computer network; Transient response; Telecommunications; Wireless; Mathematics; Statistics; Engineering; Electrical engineering","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":[0.003681193,0.0006771797,0.000879994,0.0008346042,0.0005784282,0.001288974,0.00165062,0.0009918677,0.001684664],"category_scores_gemma":[0.01886155,0.0004021419,0.0008418419,0.0006704709,0.001693357,0.002054715,0.001189191,0.001124351,0.0002719015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002902657,"about_ca_system_score_gemma":0.001356554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004357209,"about_ca_topic_score_gemma":0.001678545,"domain_scores_codex":[0.9978088,0.0005844837,0.0001123488,0.0002982351,0.0007369267,0.000459307],"domain_scores_gemma":[0.9860363,0.009354069,0.001479197,0.0007599917,0.002057491,0.000312937],"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":[0.0003788274,0.00005276263,0.002230466,0.0001438854,0.00007397711,0.0003777511,0.0005536061,0.8991231,0.01409898,0.06843041,0.0006158082,0.01392046],"study_design_scores_gemma":[0.000003518186,0.00003030893,0.0002135025,0.000005596688,0.00001252615,0.00004917152,0.00003005078,0.9956006,0.0007352802,0.003226193,0.00008579911,0.000007441666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08848217,0.0002851883,0.9067013,0.000273771,0.00002390903,0.00007995331,0.0000548983,0.00024279,0.003856089],"genre_scores_gemma":[0.9892145,0.0001192987,0.009546864,0.00003817002,0.00000977786,0.00005383109,0.00002674012,0.00002188985,0.0009689767],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004357209,"threshold_uncertainty_score":0.02106035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03687614240152245,"score_gpt":0.2785901626221066,"score_spread":0.2417140202205842,"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."}}