{"id":"W1971564594","doi":"10.1007/s00216-011-4761-5","title":"Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry","year":2011,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Discrete wavelet transform; Noise (video); Wavelet transform; Calibration; Computer science; Second-generation wavelet transform; Raman spectroscopy; Noise reduction; Artificial intelligence; Pattern recognition (psychology); Algorithm; Mathematics; Optics; Statistics; Physics; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000201356,0.0005222037,0.001053827,0.0002022495,0.000203981,0.0001192998,0.0002960021,0.0003987521,0.004411945],"category_scores_gemma":[0.0001952291,0.0004354331,0.0004966834,0.001409174,0.0009267586,0.0001814466,0.00009567253,0.0004068118,0.000007365737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007708005,"about_ca_system_score_gemma":0.00004324436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001400619,"about_ca_topic_score_gemma":0.00001446696,"domain_scores_codex":[0.9972606,0.0000134857,0.0006089019,0.001017992,0.0003563863,0.0007426478],"domain_scores_gemma":[0.9981115,0.0005972719,0.0001333026,0.0003750568,0.0001584287,0.0006244351],"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":[0.0125149,0.01227799,0.0775505,0.00841284,0.1256692,0.0004686466,0.002563328,0.000007381771,0.6171665,0.1084038,0.02281539,0.01214949],"study_design_scores_gemma":[0.003068214,0.000577384,0.001977171,0.00005245329,0.03081235,0.00005806306,0.005959403,0.04708635,0.8956443,0.01035896,0.002173184,0.002232166],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8575896,0.004762708,0.02971595,0.001307563,0.00002587089,0.0002922866,0.001472072,0.0002467225,0.1045872],"genre_scores_gemma":[0.9908963,0.0003948162,0.004003653,0.0001276826,0.00008530122,0.00002459609,0.0001738488,0.00003469655,0.004259056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2784778,"threshold_uncertainty_score":0.9998097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05244763605360843,"score_gpt":0.2947299205426221,"score_spread":0.2422822844890137,"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."}}