{"id":"W2963621379","doi":"10.1134/s0020441218010256","title":"Application of Hilbert-Huang Decomposition to Reduce Noise and Characterize for NMR FID Signal of Proton Precession Magnetometer","year":2018,"lang":"en","type":"article","venue":"Instruments and Experimental Techniques","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Hilbert–Huang transform; SIGNAL (programming language); Free induction decay; Magnetometer; Noise (video); Noise reduction; Nuclear magnetic resonance; Physics; Hilbert transform; Hilbert spectral analysis; Acoustics; Computational physics; Algorithm; Mathematical analysis; Mathematics; Computer science; Energy (signal processing); Spectral density; Magnetic field; Spin echo; Statistics; Artificial intelligence; Quantum mechanics; Magnetic resonance imaging","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":[0.0008408036,0.0006184157,0.0003569603,0.0006674728,0.0002689355,0.0004104236,0.0003458686,0.0004013079,0.0008501335],"category_scores_gemma":[0.001518164,0.0001410928,0.0003912754,0.000577213,0.0003551169,0.0007192605,0.0003262463,0.0004445949,0.0002531573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002478439,"about_ca_system_score_gemma":0.0004393005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009727079,"about_ca_topic_score_gemma":0.0008216521,"domain_scores_codex":[0.9995795,0.0001153079,0.00002047094,0.00007256085,0.0001798524,0.00003231071],"domain_scores_gemma":[0.9996517,0.0001518324,0.00002459372,0.00002990358,0.0001273758,0.00001460242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00039981,0.0002159041,0.004431492,0.0003367693,0.0001119556,0.0002651791,0.0004445802,0.07882472,0.303542,0.01319255,0.001953413,0.5962816],"study_design_scores_gemma":[0.00001952074,0.0001967797,0.005063226,0.0000123588,0.00004437707,0.0002087937,0.00008805526,0.8906046,0.09527441,0.004418274,0.004021077,0.00004862358],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03412609,0.0001797517,0.9645303,0.00006596621,0.00002356062,0.00002179313,0.00003718929,0.0003203601,0.0006949745],"genre_scores_gemma":[0.4682192,0.0004302636,0.5292387,0.00006079393,0.00005341235,0.000097116,0.0002576204,0.0001120716,0.001530881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009727079,"threshold_uncertainty_score":0.004446626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01001057510186266,"score_gpt":0.3696239461975434,"score_spread":0.3596133710956807,"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."}}