{"id":"W1988369819","doi":"10.1109/tvt.2011.2177871","title":"Cooperative Sensing With Correlated Local Decisions in Cognitive Radio Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Fusion center; Constraint (computer-aided design); False alarm; Integer (computer science); Value (mathematics); Minification; Correlation; Nonlinear system; Expected value; Mathematics; Computer science; Mathematical optimization; Algorithm; Artificial intelligence; 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":[0.004261586,0.00105857,0.001259764,0.0007500527,0.0005396085,0.001084561,0.001056362,0.0009160928,0.000478318],"category_scores_gemma":[0.01279304,0.0005321281,0.000623585,0.0007422723,0.001887163,0.001261182,0.001066752,0.0006993572,0.00009529471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001378469,"about_ca_system_score_gemma":0.001321674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004323641,"about_ca_topic_score_gemma":0.003482322,"domain_scores_codex":[0.9976585,0.0009399836,0.00005591064,0.0003217647,0.0006324534,0.0003913492],"domain_scores_gemma":[0.9894491,0.008343847,0.001127808,0.00029999,0.000614019,0.0001651015],"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":[0.0000470826,0.00002346783,0.000570672,0.00001947495,0.00003384351,0.00009852145,0.00004939958,0.9906492,0.0008465461,0.004193862,0.00006008572,0.003407835],"study_design_scores_gemma":[0.000006427317,0.00003364178,0.0001590425,0.000003112651,0.00001019885,0.00002204403,0.00001372709,0.99695,0.0004590106,0.00231005,0.0000269842,0.000005806503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.22813,0.0004157776,0.7689471,0.0001872813,0.00002374203,0.000045408,0.00002150181,0.0001541798,0.002074901],"genre_scores_gemma":[0.9867277,0.0001205263,0.01259265,0.00004141033,0.00001065141,0.00003183681,0.000008376943,0.00001099971,0.0004557654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004323641,"threshold_uncertainty_score":0.02253765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01633241092905183,"score_gpt":0.2210160623842282,"score_spread":0.2046836514551763,"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."}}