{"id":"W2047939401","doi":"10.1109/glocom.2006.111","title":"CTH14-1: On the Integrated Cross-Noise Component in Correlation Receivers","year":2006,"lang":"en","type":"article","venue":"Globecom","topic":"Ultra-Wideband Communications Technology","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Autocorrelation; Gaussian noise; Noise (video); Component (thermodynamics); Probability density function; SIGNAL (programming language); Cross-correlation; Convergence (economics); Gaussian; Uncorrelated; Noise measurement; Algorithm; Value noise; Statistics; Computer science; Mathematics; Noise floor; Physics; Noise reduction; Artificial intelligence","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":[0.00009875973,0.00009458757,0.00008642886,0.00009349348,0.0000578539,0.00002593408,0.0002976629,0.00009346669,0.00007340845],"category_scores_gemma":[0.00002950398,0.00007686861,0.00002713288,0.0003103492,0.00006135731,0.00005032137,0.00002215297,0.0002986371,0.0001918723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001779985,"about_ca_system_score_gemma":0.000007794325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004893392,"about_ca_topic_score_gemma":0.0005793794,"domain_scores_codex":[0.9994832,0.00003430866,0.0001811814,0.00009091769,0.0000658265,0.0001445689],"domain_scores_gemma":[0.9993829,0.0001227824,0.00002386798,0.0004375815,0.0000207943,0.00001211039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0000569998,0.0003265977,0.1361027,0.00001767121,0.00005140743,0.00001231874,0.000523757,0.7181694,0.01464018,0.05428408,0.05085024,0.0249647],"study_design_scores_gemma":[0.001191094,0.00006306548,0.6252452,0.0001084598,0.00001478636,0.00001360494,0.0003639484,0.2236861,0.00734954,0.007931348,0.1335339,0.000499056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752607,0.0001664586,0.00108569,0.001007949,0.0001806122,0.0001669318,0.000008714519,0.0004174804,0.02170551],"genre_scores_gemma":[0.9993703,0.00003704557,0.0002849404,0.00005352178,0.00000974894,0.00002886507,0.00004583449,0.00001235509,0.000157427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4944833,"threshold_uncertainty_score":0.3134611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006992885321791408,"score_gpt":0.2050454258952778,"score_spread":0.1980525405734864,"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."}}