{"id":"W3141578425","doi":"10.1109/twc.2010.5427414","title":"BER optimal linear combiner for signal detection in symmetric alpha-stable noise: small values of alpha","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Ultra-Wideband Communications Technology","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Signal-to-noise ratio (imaging); Noise (video); Rake receiver; Mathematics; Diversity combining; Bit error rate; Rake; SIGNAL (programming language); Algorithm; Detection theory; Maximal-ratio combining; Fading; Computer science; Statistics; Telecommunications; Decoding methods; Artificial intelligence; Detector","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003157931,0.0002556418,0.0003583478,0.0009170665,0.0003335479,0.0000246563,0.001401622,0.0003346321,0.0000389708],"category_scores_gemma":[0.00002661866,0.0002965058,0.0001579351,0.00157042,0.0002928861,0.0001968455,0.00001318226,0.001165257,0.00001959512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001007469,"about_ca_system_score_gemma":0.00005260425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002196614,"about_ca_topic_score_gemma":0.002734478,"domain_scores_codex":[0.9985559,0.00009165904,0.0006413812,0.0002308083,0.0001396446,0.0003406329],"domain_scores_gemma":[0.9961845,0.0009549881,0.0001060632,0.002417773,0.0002602666,0.00007643778],"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":[0.0001095298,0.002510866,0.0001841962,0.0001579802,0.0003348999,6.547926e-7,0.0009137451,0.1914807,0.5994951,0.004966377,0.00009208823,0.1997539],"study_design_scores_gemma":[0.001315229,0.0001689867,0.0004358446,0.00005977294,0.00009873478,0.00001274334,0.0001891988,0.5023238,0.4918833,0.0004864607,0.002612787,0.0004131369],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4277851,0.0002293995,0.5693235,0.0003811397,0.0002990622,0.000710013,0.0001163676,0.0005041041,0.0006513292],"genre_scores_gemma":[0.9655706,0.0006559173,0.03263628,0.00002347762,0.00001422146,0.0008782733,0.00003539383,0.00007548198,0.0001103953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5377855,"threshold_uncertainty_score":0.9999487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01884463964871762,"score_gpt":0.2471356502981956,"score_spread":0.228291010649478,"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."}}