{"id":"W2106132463","doi":"10.1186/1687-1499-2013-165","title":"Threshold optimization of a finite sample-based cognitive radio network using energy detector","year":2013,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Cognitive radio; Computer science; Fusion center; Energy (signal processing); Detector; Algorithm; Minification; Interference (communication); Statistics; Mathematics; Telecommunications; Wireless","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001911186,0.0006618343,0.00108497,0.0004571342,0.0003183614,0.001151293,0.001582444,0.0008560637,0.0008253374],"category_scores_gemma":[0.004994378,0.0004631569,0.0004895278,0.0004426246,0.001132642,0.001227392,0.001207412,0.0007835799,0.0001091596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00155456,"about_ca_system_score_gemma":0.00112095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00177449,"about_ca_topic_score_gemma":0.00130151,"domain_scores_codex":[0.9987085,0.000504522,0.00004100911,0.0002384662,0.0003446534,0.0001629427],"domain_scores_gemma":[0.9973657,0.001931023,0.0002240527,0.00009078751,0.0003029551,0.00008555107],"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":[0.0001412452,0.00004217943,0.0004164088,0.00004582046,0.00004400396,0.00007645033,0.00003814121,0.9745452,0.003065916,0.009690082,0.0001494631,0.01174506],"study_design_scores_gemma":[0.000005253364,0.00002141996,0.000043346,0.00000216035,0.000005703048,0.000009519301,0.000003686645,0.9981583,0.0004081739,0.001305268,0.00003389956,0.000003230545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04684945,0.0001821802,0.9510945,0.000116571,0.00001838464,0.00002751026,0.00001869331,0.0001082933,0.001584326],"genre_scores_gemma":[0.9554681,0.000103788,0.0432437,0.000059974,0.00001008857,0.00005575109,0.00002484205,0.00001396117,0.001019728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001911186,"threshold_uncertainty_score":0.01127923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03675592329315886,"score_gpt":0.2585560136467335,"score_spread":0.2218000903535746,"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."}}