{"id":"W1990718802","doi":"10.1117/12.887211","title":"Application of wavelet transforms in de-noising optical emission transient signals generated from microsamples introduced into a microplasma and comparison with Fourier- and Hartley-transforms","year":2011,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wavelet transform; Harmonic wavelet transform; Microplasma; Wavelet; Fast Fourier transform; Constant Q transform; Transient (computer programming); Fourier transform; Discrete wavelet transform; Electronic engineering; Computer science; Materials science; Physics; Algorithm; Mathematics; Artificial intelligence; Engineering; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000803887,0.00026906,0.0004733164,0.0001267833,0.00007124022,0.0001098815,0.0005895762,0.0001660428,0.000001630488],"category_scores_gemma":[0.0001072525,0.0002016157,0.0001605838,0.0003297112,0.0002649312,0.0005913734,0.0000736082,0.0002641893,1.004695e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007165688,"about_ca_system_score_gemma":0.00004124213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009856187,"about_ca_topic_score_gemma":0.000001749823,"domain_scores_codex":[0.9981452,1.046812e-7,0.0006855062,0.0004530008,0.0003975635,0.0003185745],"domain_scores_gemma":[0.9988076,0.000140036,0.0002270098,0.00006606522,0.0006299143,0.0001293898],"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":[0.0003043155,0.0001419315,0.0008791223,0.0002927311,0.0001340757,2.107129e-7,0.004307376,0.00004222727,0.9514976,0.03455226,0.00003607503,0.007812071],"study_design_scores_gemma":[0.001476367,0.0003490238,0.002317362,0.0002725013,0.00006830847,0.0000180676,0.0006323202,0.1238674,0.8683735,0.002310434,0.00007942718,0.0002352468],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9119509,0.0001469895,0.08659866,0.0007115159,0.00003607308,0.0004211434,0.00001303346,0.00003380539,0.00008791338],"genre_scores_gemma":[0.5353378,0.00004088209,0.4645074,0.00002731963,0.00003784221,0.00002431016,0.000003526097,0.00001700605,0.000003910088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3779087,"threshold_uncertainty_score":0.8221651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01453162290343807,"score_gpt":0.2362925665486354,"score_spread":0.2217609436451973,"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."}}