{"id":"W2012937697","doi":"10.1038/srep08017","title":"Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra","year":2015,"lang":"en","type":"article","venue":"Scientific Reports","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto; York University","keywords":"Wavelet; Noise reduction; Filter (signal processing); Wiener filter; Computer science; Pattern recognition (psychology); Algorithm; Mathematics; Artificial intelligence; Computer vision","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004931205,0.0001181862,0.0001687925,0.00006882313,0.0001733172,0.00005076648,0.00008695663,0.00003068438,0.00008863915],"category_scores_gemma":[0.00002125641,0.0001039852,0.0001010741,0.0001874746,0.0001683363,0.0001200922,0.00004186767,0.00007670377,0.0000103762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002561245,"about_ca_system_score_gemma":0.0002864902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002607221,"about_ca_topic_score_gemma":0.00002958308,"domain_scores_codex":[0.998603,0.00001720274,0.0003712079,0.0004480652,0.0003225055,0.0002379841],"domain_scores_gemma":[0.9988499,0.00002195298,0.0002862335,0.0004245691,0.0002738987,0.000143469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001899069,0.001239125,0.003862049,0.00003155376,0.0001592551,0.0000297428,0.001956324,0.003291733,0.9172936,0.05044068,0.01185923,0.009646814],"study_design_scores_gemma":[0.0004979271,0.00007828597,0.0001466766,0.00004280936,0.00003156792,0.000005583493,0.0002353561,0.01648006,0.8886406,0.08247701,0.01113156,0.0002325832],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8019107,0.00002115426,0.1932374,0.0001919075,0.001073048,0.001070731,0.00008078865,0.0000278598,0.002386386],"genre_scores_gemma":[0.9531429,2.480014e-8,0.04472791,0.000004927266,0.0003492875,0.0001183091,0.0001800147,0.00001376551,0.001462893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1512322,"threshold_uncertainty_score":0.4240395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02886298051744355,"score_gpt":0.3103634780772063,"score_spread":0.2815004975597627,"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."}}