{"id":"W2279375385","doi":"10.1109/isspit.2015.7394325","title":"Novel energy efficient strategies for cooperative spectrum sensing in cognitive radio networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Cognitive radio; Computer science; Overhead (engineering); Efficient energy use; Fusion rules; Energy (signal processing); Computer network; Cascading Style Sheets; Spectrum (functional analysis); Distributed computing; Telecommunications; Wireless; Artificial intelligence; 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":[0.0005638744,0.000539972,0.0005371944,0.000276851,0.0003270204,0.0006633887,0.0008656263,0.0005028098,0.0004882796],"category_scores_gemma":[0.001542166,0.0001906697,0.0002824573,0.000258322,0.0006038455,0.0007608602,0.0005463645,0.0004123315,0.0001185587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002777523,"about_ca_system_score_gemma":0.0004153249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005724892,"about_ca_topic_score_gemma":0.0007783355,"domain_scores_codex":[0.999595,0.0001202603,0.00002347928,0.0000748946,0.0001310774,0.00005530799],"domain_scores_gemma":[0.999485,0.0002949344,0.00008048828,0.00004030197,0.00007726749,0.00002187434],"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":[0.0002059158,0.0002024058,0.0007148519,0.0001628333,0.00009649195,0.0003220933,0.0002874297,0.6929367,0.033335,0.08797188,0.001326799,0.1824376],"study_design_scores_gemma":[0.00002069167,0.0001218506,0.0001582679,0.000009842739,0.00001605076,0.0001262672,0.00003219228,0.9785494,0.002545618,0.01772542,0.0006796767,0.00001460439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04704215,0.0008213768,0.9474222,0.0001155809,0.00004286855,0.00003441304,0.00001146557,0.00006802271,0.004441843],"genre_scores_gemma":[0.9524155,0.0003593603,0.04596622,0.00006611107,0.00003001621,0.00004930765,0.00001095775,0.000008522212,0.00109402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008656263,"threshold_uncertainty_score":0.00298208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03167262260287485,"score_gpt":0.260844761394991,"score_spread":0.2291721387921162,"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."}}