{"id":"W2164283738","doi":"10.1016/j.sigpro.2012.04.014","title":"A robust detector of known signal in non-Gaussian noise using threshold systems","year":2012,"lang":"en","type":"article","venue":"Signal Processing","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Gaussian noise; Probability density function; Noise (video); Detector; Value noise; Detection theory; Noise spectral density; Noise measurement; Additive white Gaussian noise; Robustness (evolution); Gradient noise; Matched filter; White noise; Mathematics; Algorithm; Gaussian; Noise floor; Computer science; Statistics; Noise reduction; Noise figure; Artificial intelligence; Physics; Telecommunications; Bandwidth (computing)","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":[0.0005270961,0.000212311,0.0003172748,0.0002387433,0.0001617689,0.0002423696,0.0004338185,0.0001315746,0.00001759227],"category_scores_gemma":[0.000007934823,0.0001943547,0.00007485726,0.001085847,0.0000632997,0.001089827,0.0001256638,0.0002684689,0.00000859516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001070054,"about_ca_system_score_gemma":0.0001143821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008578293,"about_ca_topic_score_gemma":0.000004836357,"domain_scores_codex":[0.9981087,0.00005970922,0.0004934627,0.0003318375,0.0004022837,0.000603954],"domain_scores_gemma":[0.9991352,0.00004312809,0.0002694218,0.0002312359,0.0001279503,0.0001930752],"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":[0.0001052245,0.0007825035,0.01029591,0.000852802,0.00006483551,0.00007568608,0.00273996,0.6707364,0.07490771,0.001146773,0.0001698595,0.2381223],"study_design_scores_gemma":[0.0003472116,0.00005171196,0.0008272843,0.0003339915,0.00001018118,0.00004931216,0.0001424004,0.9932657,0.004551341,0.00006595073,0.0001016021,0.0002533088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07986338,0.001436308,0.9171994,0.00001632491,0.0003555544,0.0001928718,0.00000417365,0.00009200169,0.0008399338],"genre_scores_gemma":[0.9884807,0.000002591992,0.01108892,0.0000275475,0.0003403857,0.000009190496,0.000001686972,0.00001897033,0.00002995152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9086174,"threshold_uncertainty_score":0.7925555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03320405632123942,"score_gpt":0.2486538770088694,"score_spread":0.21544982068763,"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."}}