{"id":"W1910481364","doi":"10.1002/wcm.2615","title":"A selective decision–fusion rule for cooperative spectrum sensing using energy detection","year":2015,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of Ottawa","funders":"","keywords":"Computer science; Fusion center; Cognitive radio; Benchmark (surveying); Decoding methods; Energy (signal processing); Decision rule; Fuse (electrical); Fusion; Real-time computing; Data mining; Artificial intelligence; Telecommunications; Wireless; Statistics","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":[0.003607747,0.0006695502,0.001078558,0.0006103552,0.0004933533,0.001355466,0.001713473,0.00107639,0.001127661],"category_scores_gemma":[0.009384626,0.0002801495,0.0004603013,0.0005331537,0.001425412,0.0009695385,0.0009220809,0.001207853,0.0003675058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008524423,"about_ca_system_score_gemma":0.001053739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002153318,"about_ca_topic_score_gemma":0.001672781,"domain_scores_codex":[0.9975763,0.0007493382,0.000210386,0.0005399316,0.0007097318,0.0002143719],"domain_scores_gemma":[0.9932729,0.004637134,0.0005085092,0.0004404593,0.001021333,0.0001195989],"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":[0.0003996905,0.0001539139,0.0008512529,0.0001429855,0.0001165941,0.0004624885,0.0002864983,0.8222204,0.01028763,0.0631685,0.001530912,0.1003792],"study_design_scores_gemma":[0.00002057088,0.00005978249,0.0001007062,0.000008921435,0.00001310592,0.00007501744,0.000009663321,0.9891479,0.001667732,0.008550986,0.0003358062,0.000009775337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02803413,0.0001874309,0.968441,0.0001343211,0.00004302605,0.00007700639,0.00003241063,0.0001177148,0.002932906],"genre_scores_gemma":[0.9054273,0.0001329855,0.09319802,0.00009178096,0.00003803451,0.000116203,0.00004468275,0.0000148369,0.0009362871],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003607747,"threshold_uncertainty_score":0.0190798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03018737393623534,"score_gpt":0.2845042730922828,"score_spread":0.2543168991560475,"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."}}