{"id":"W2002693740","doi":"10.1109/antem.2014.6887672","title":"Application of cognitive radio principles to wireless channel sounding","year":2014,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Cognitive radio; Channel sounding; Channel (broadcasting); Computer science; Interference (communication); Wireless; Depth sounding; Control channel; Transmission (telecommunications); Spectrum analyzer; Electronic engineering; Telecommunications; Computer network; Engineering; MIMO; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002943906,0.00009794055,0.0001656572,0.0001033669,0.00008708756,0.00005461851,0.0002502784,0.0000321439,0.000002204043],"category_scores_gemma":[0.00006279556,0.00009082376,0.00004343329,0.0003790478,0.00002834964,0.0001402369,0.0001253792,0.0000584812,0.00002356912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002465433,"about_ca_system_score_gemma":0.00002091327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004228673,"about_ca_topic_score_gemma":0.00003330283,"domain_scores_codex":[0.9990862,0.00004580645,0.0001816521,0.0003094695,0.0001642556,0.0002126199],"domain_scores_gemma":[0.9992051,0.0002895286,0.00007915385,0.0002180065,0.0001174924,0.00009078801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001566848,0.00007190614,0.0009217346,0.00001641083,0.00003014587,0.000001607988,0.001748355,0.0006201804,0.003872765,0.4869393,0.00006061391,0.5057013],"study_design_scores_gemma":[0.0002800491,0.0001133532,0.007335472,0.00008915563,0.000007868332,0.00001203773,0.00009655477,0.9744266,0.01365995,0.00299403,0.0007763112,0.0002085952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09313659,0.00001438044,0.8972074,0.0002934844,0.00009394188,0.0001939409,5.63488e-7,0.0000877221,0.008971986],"genre_scores_gemma":[0.9918255,0.000004567014,0.007650383,0.0002518047,0.0001375672,0.000009667283,0.00000185957,0.000007589587,0.0001110529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9738064,"threshold_uncertainty_score":0.3703686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01701250012431302,"score_gpt":0.2447682650011637,"score_spread":0.2277557648768507,"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."}}