{"id":"W4238386380","doi":"10.32920/14639013","title":"Cognitive Spectrum Sensing with Multiple Primary Users in Rayleigh Fading Channels","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cognitive radio; Multipath propagation; Fading; Detector; Rayleigh fading; Computer science; Eigenvalues and eigenvectors; Electronic engineering; Spectrum (functional analysis); Algorithm; Telecommunications; Wireless; Physics; Channel (broadcasting); Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002627407,0.000410494,0.0005467614,0.0002827413,0.0001212783,0.0009033785,0.0004416898,0.0002772286,0.00002477902],"category_scores_gemma":[0.00004383881,0.0003795537,0.000133938,0.0007770399,0.00005977665,0.0003374314,0.001117668,0.0009852127,0.0000125655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002418158,"about_ca_system_score_gemma":0.0002321006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004565548,"about_ca_topic_score_gemma":0.0004365699,"domain_scores_codex":[0.9972888,0.0001461254,0.0003898554,0.001165268,0.0004304371,0.0005794672],"domain_scores_gemma":[0.9986486,0.0002555204,0.0001903456,0.0005757611,0.0001712841,0.0001585131],"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":[0.0004018545,0.0009479583,0.008167692,0.0006705395,0.001144896,0.009789481,0.009420069,0.7349694,0.0005265194,0.001429024,0.0009838917,0.2315486],"study_design_scores_gemma":[0.001366237,0.00008839197,0.003420994,0.001061204,0.00002444933,0.0001782827,0.0007037228,0.9868397,0.004693973,0.0004884088,0.000170943,0.0009637367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08049076,0.0001145099,0.9134157,0.0002974432,0.00148046,0.0004234746,0.00001163746,0.00035754,0.003408422],"genre_scores_gemma":[0.9485836,0.00005132301,0.05028385,0.0004615083,0.0002121074,0.00001140798,0.00009961881,0.00003079596,0.0002657699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8680928,"threshold_uncertainty_score":0.9998657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01712999392677825,"score_gpt":0.2262354879552649,"score_spread":0.2091054940284866,"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."}}