{"id":"W4375937979","doi":"10.36227/techrxiv.18586103","title":"Performance of Reconfigurable Intelligent Surfaces in the Presence of Generalized Gaussian Noise","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Rayleigh fading; Gaussian; Noise (video); Gaussian noise; Fading; Expression (computer science); Modulation (music); Mathematics; Symbol (formal); Order (exchange); Applied mathematics; Diversity gain; Algorithm; Topology (electrical circuits); Telecommunications; Computer science; Channel (broadcasting); Physics; Combinatorics; Acoustics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006579583,0.0006783077,0.0006326905,0.0002960429,0.000340816,0.0009313812,0.000455444,0.0008227669,0.0004749457],"category_scores_gemma":[0.002719009,0.0001358575,0.0002869711,0.0003535589,0.001386015,0.0007443879,0.0008775248,0.0003696173,0.0001444424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004876512,"about_ca_system_score_gemma":0.000357657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001228265,"about_ca_topic_score_gemma":0.0005736421,"domain_scores_codex":[0.9992127,0.0002623089,0.00002304894,0.0001149819,0.0001909984,0.0001959373],"domain_scores_gemma":[0.99793,0.001264528,0.0003143087,0.000184988,0.000233639,0.00007254674],"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":[0.0006020198,0.0000354055,0.00287257,0.00007497139,0.00005971964,0.0005490596,0.0001696172,0.9164444,0.06034024,0.00812749,0.0001537394,0.01057078],"study_design_scores_gemma":[0.00001131362,0.0001998719,0.0007585451,0.000008778151,0.00001958537,0.0001459934,0.00008117267,0.9762577,0.02077554,0.001590547,0.0001299975,0.00002087615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9232574,0.0002935863,0.0713772,0.0001243969,0.00002384786,0.000007523037,0.00002324026,0.0001416587,0.004751119],"genre_scores_gemma":[0.9984502,0.00004406592,0.001232576,0.00001108855,0.000002854762,0.00000243313,0.000006083432,0.000003540663,0.0002471178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001228265,"threshold_uncertainty_score":0.003538191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03098326526902864,"score_gpt":0.2654795854369045,"score_spread":0.2344963201678759,"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."}}