{"id":"W2968875843","doi":"10.1177/0003702819860121","title":"Improved Vancouver Raman Algorithm Based on Empirical Mode Decomposition for Denoising Biological Samples","year":2019,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Hilbert–Huang transform; Raman spectroscopy; Noise reduction; Algorithm; Decomposition; Mode (computer interface); Analytical Chemistry (journal); Materials science; Computer science; Chemistry; Optics; Physics; Artificial intelligence; Telecommunications; Environmental chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00156301,0.001080034,0.0007934312,0.001674459,0.0005080846,0.001278135,0.001658588,0.001258591,0.002468142],"category_scores_gemma":[0.002326756,0.0004508961,0.001308674,0.001242448,0.0005789023,0.001274329,0.001204737,0.001535213,0.00123159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005188882,"about_ca_system_score_gemma":0.00146276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003581977,"about_ca_topic_score_gemma":0.005434636,"domain_scores_codex":[0.9989635,0.0001843412,0.00005881418,0.0001903159,0.0005384068,0.00006469776],"domain_scores_gemma":[0.9989747,0.0002883962,0.00007564034,0.0001092064,0.0005076996,0.0000444258],"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":[0.0002301461,0.0001158532,0.001682302,0.0003055084,0.0001546179,0.0001933625,0.0002117732,0.1024585,0.115038,0.02095735,0.005186412,0.7534662],"study_design_scores_gemma":[0.000026357,0.00008781982,0.0006925446,0.00002024509,0.00003043084,0.0003479657,0.00005824892,0.9583224,0.02643565,0.005158153,0.008755946,0.00006429718],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004349503,0.0002367224,0.9940554,0.00006667206,0.0000318789,0.0000285949,0.00003502665,0.0004748477,0.0007212187],"genre_scores_gemma":[0.03334652,0.0002989286,0.9630151,0.00008106863,0.00002048306,0.0001084289,0.0002102536,0.0001505703,0.002768713],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003581977,"threshold_uncertainty_score":0.008266091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01668122383217378,"score_gpt":0.3829027908895153,"score_spread":0.3662215670573415,"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."}}