{"id":"W2883508777","doi":"10.3233/jifs-169740","title":"Parallel weak signal detection algorithm under Gauss noise interference","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Signal transfer function; Noise reduction; Step detection; SIGNAL (programming language); Algorithm; Noise (video); Interference (communication); Detection theory; Matched filter; Mathematics; Wavelet; Filter (signal processing); Computer science; Pattern recognition (psychology); Artificial intelligence; Analog signal; Digital signal processing; Computer vision; Telecommunications; Channel (broadcasting)","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":[0.001099871,0.0009549764,0.001171266,0.001691517,0.0007004404,0.001027617,0.00139846,0.00120564,0.001906917],"category_scores_gemma":[0.002665634,0.0004848888,0.0008521188,0.0009597035,0.0008479172,0.001617488,0.001467683,0.0009486937,0.0009883214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004387652,"about_ca_system_score_gemma":0.001063767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001438645,"about_ca_topic_score_gemma":0.001052702,"domain_scores_codex":[0.9986634,0.0001434549,0.0000973713,0.000333515,0.0006658472,0.00009639948],"domain_scores_gemma":[0.9987596,0.0002557392,0.0001291147,0.000147325,0.0006403827,0.00006787436],"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":[0.0007882395,0.0001387058,0.003564236,0.0002929326,0.0001439789,0.000382966,0.000373023,0.06335386,0.1450437,0.01457712,0.002203344,0.7691379],"study_design_scores_gemma":[0.00005376457,0.0001805093,0.001173116,0.0000170162,0.00006534377,0.0006477751,0.0000526379,0.9134663,0.07327813,0.006981472,0.004032605,0.00005131122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01480449,0.0001356172,0.9831387,0.00006333711,0.00003880266,0.00004421221,0.00001979058,0.0004792268,0.001275784],"genre_scores_gemma":[0.2817837,0.0004444257,0.7101837,0.0001530138,0.00009977241,0.0001901864,0.0002311761,0.0001364439,0.006777621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001906917,"threshold_uncertainty_score":0.006379306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02863487043742833,"score_gpt":0.2812367658329545,"score_spread":0.2526018953955262,"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."}}