{"id":"W2106929822","doi":"10.1109/icassp.2002.5745503","title":"Enhanced denoising scheme for NMR signals","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Acoustics Speech and Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Noise (video); Noise reduction; SIGNAL (programming language); Signal-to-noise ratio (imaging); Scheme (mathematics); Energy (signal processing); Time domain; Nuclear magnetic resonance; Frequency domain; Algorithm; Physics; Computer science; Acoustics; Mathematics; Artificial intelligence; Telecommunications; Mathematical analysis; Quantum mechanics; Computer vision","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":[0.0007751886,0.0005288576,0.0005757164,0.0004652011,0.0002694676,0.0004762679,0.0008053024,0.001046721,0.002328361],"category_scores_gemma":[0.00124798,0.0002183557,0.0004776655,0.0003777134,0.0003770036,0.001164644,0.0007941179,0.0009028491,0.0009490178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003466065,"about_ca_system_score_gemma":0.0003238725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000384376,"about_ca_topic_score_gemma":0.0006538975,"domain_scores_codex":[0.9995804,0.00009163746,0.00002815206,0.00007541543,0.0001970767,0.00002735477],"domain_scores_gemma":[0.9995794,0.00009759603,0.00004018824,0.0001001966,0.0001596533,0.00002305876],"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":[0.0004758692,0.00009592066,0.0002645826,0.0003084032,0.00005653413,0.0002342723,0.0002091281,0.07491506,0.5615659,0.06936581,0.002620522,0.289888],"study_design_scores_gemma":[0.00003971605,0.0001870267,0.0001986316,0.00002565688,0.0000354668,0.000450937,0.00001609288,0.8068804,0.1619008,0.009368633,0.02084837,0.0000482707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006953841,0.0002138785,0.9915071,0.00004545345,0.00006566285,0.00002789274,0.00003134935,0.0002032344,0.0009515507],"genre_scores_gemma":[0.09915321,0.0004905979,0.8924081,0.0001083308,0.00006566913,0.00008107973,0.0001764969,0.00007172355,0.007444763],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002328361,"threshold_uncertainty_score":0.007789135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08085480537032708,"score_gpt":0.3350147836717813,"score_spread":0.2541599783014542,"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."}}