{"id":"W2544236455","doi":"10.1109/jsyst.2016.2615019","title":"Joint Quantization and Confidence-Based Generalized Combining Scheme for Cooperative Spectrum Sensing","year":2016,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Fusion center; Quantization (signal processing); False alarm; Computer science; Cascading Style Sheets; Algorithm; Detector; Scheme (mathematics); Overhead (engineering); Mathematics; Wireless; Artificial intelligence; Telecommunications","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":[0.001836941,0.001007569,0.0009137226,0.0007345385,0.0007918431,0.001250291,0.001690222,0.000884354,0.001146536],"category_scores_gemma":[0.005477428,0.0003066267,0.0007051734,0.001374032,0.001125047,0.001995154,0.002204399,0.001216924,0.0002743927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007605652,"about_ca_system_score_gemma":0.00099443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00140507,"about_ca_topic_score_gemma":0.001558967,"domain_scores_codex":[0.9973699,0.0006122976,0.0001628538,0.0004017128,0.001240131,0.0002131034],"domain_scores_gemma":[0.9977061,0.0008224706,0.0002950182,0.0004287216,0.0006431554,0.0001045516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006998527,0.0001986592,0.002266193,0.0002958624,0.0002535951,0.0004505645,0.0007135332,0.3810002,0.07660144,0.0881516,0.002957139,0.4464113],"study_design_scores_gemma":[0.00007179056,0.0003840653,0.0008632471,0.00002702003,0.0001038826,0.0004323621,0.00007054906,0.9474874,0.0161491,0.03138347,0.002913933,0.0001131057],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02464893,0.0004449263,0.9723986,0.0001661561,0.00005364494,0.00006933464,0.00004120647,0.00020613,0.001971066],"genre_scores_gemma":[0.8661739,0.000303144,0.1319015,0.0001937799,0.00007648872,0.00009281952,0.00009098205,0.00002366287,0.001143622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001836941,"threshold_uncertainty_score":0.009714782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03800295431089565,"score_gpt":0.2577400018290806,"score_spread":0.2197370475181849,"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."}}