{"id":"W1984463679","doi":"10.1109/wcnc.2010.5506444","title":"Analysis of Wireless Communication Systems in the Presence of Non-Gaussian Impulsive Noise and Gaussian Noise","year":2010,"lang":"en","type":"article","venue":"","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Gaussian noise; Noise (video); Gaussian; Additive white Gaussian noise; Computer science; Noise measurement; Gradient noise; Impulse noise; Wireless; Mathematics; Statistical physics; Algorithm; Noise floor; Telecommunications; Channel (broadcasting); 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":[0.0004339549,0.0001017278,0.0002765433,0.0002351278,0.00004052993,0.00002251673,0.0006508351,0.00007029987,0.00001450997],"category_scores_gemma":[0.00002906943,0.00007393883,0.00005722747,0.000859252,0.0001185518,0.0001214088,0.00009331053,0.000238927,0.000001030003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008625441,"about_ca_system_score_gemma":0.00001299172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00158863,"about_ca_topic_score_gemma":0.002639097,"domain_scores_codex":[0.9992045,0.0000812147,0.000385071,0.00009298028,0.0001203967,0.0001158168],"domain_scores_gemma":[0.9981732,0.0002611225,0.00009677977,0.001367483,0.00006303495,0.00003838849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008516024,0.001147309,0.3732116,0.0009365896,0.002174844,0.000006198297,0.0379878,0.08264077,0.4134992,0.06657861,0.001012766,0.02071909],"study_design_scores_gemma":[0.0002290094,0.00001606182,0.3474669,0.00005433992,0.0001666113,0.000002022863,0.001410464,0.6488687,0.001394532,0.00003568602,0.0002317034,0.0001239287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910881,0.0005473141,0.000850593,0.0001611437,0.00004286737,0.0002281854,0.00002184165,0.00002159606,0.00703836],"genre_scores_gemma":[0.998769,0.0004715608,0.0006529146,0.000007350162,0.000006384824,0.0000347283,0.00002146656,0.000009557551,0.0000270531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.566228,"threshold_uncertainty_score":0.3015138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00793219072470428,"score_gpt":0.2460079441858344,"score_spread":0.2380757534611301,"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."}}