{"id":"W2044066528","doi":"10.1109/tcomm.2012.071812.100789","title":"Suboptimal Detectors for Alpha-Stable Noise: Simplifying Design and Improving Performance","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Detector; Limiter; Gaussian; Computer science; Noise (video); Gaussian noise; Algorithm; Electronic engineering; Binary number; Signal-to-noise ratio (imaging); Mathematics; Engineering; Telecommunications; Physics; Artificial intelligence; Arithmetic","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":[0.0004108767,0.0001592354,0.0001406774,0.0001323259,0.001211851,0.0001675006,0.0007031318,0.00008204195,0.000007796094],"category_scores_gemma":[0.000008829376,0.0001654327,0.00007282053,0.000400782,0.00008215882,0.000887722,0.00001459899,0.0002703283,0.00001568517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007145781,"about_ca_system_score_gemma":0.00003471212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000219092,"about_ca_topic_score_gemma":0.000005437311,"domain_scores_codex":[0.9989137,0.00009208666,0.000243036,0.0002206751,0.0001279266,0.0004025733],"domain_scores_gemma":[0.9979383,0.0005562808,0.00008673011,0.001144461,0.0001042331,0.0001700244],"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":[0.00007409097,0.0006912459,0.000075574,0.00006185591,0.0001150877,2.945585e-7,0.001126058,0.1099126,0.007799658,0.002301349,0.0003214896,0.8775207],"study_design_scores_gemma":[0.0003934525,0.0001377836,0.0002458207,0.00001624736,0.00003086626,0.00002117336,0.00004800158,0.9800295,0.01464321,0.00008242777,0.004114294,0.0002371958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00440294,0.0002949417,0.9937494,0.0003049461,0.0004186346,0.0004168975,0.00001868126,0.0002616631,0.0001318287],"genre_scores_gemma":[0.7578477,0.0002058598,0.241522,0.00008769896,0.00002805949,0.0001901957,0.000002738976,0.0000146572,0.0001010805],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8772835,"threshold_uncertainty_score":0.9320701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04774013833522976,"score_gpt":0.2663695672663515,"score_spread":0.2186294289311217,"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."}}