{"id":"W2031041217","doi":"10.1109/jlt.2011.2168599","title":"Reduction in the Number of Averages Required in BOTDA Sensors Using Wavelet Denoising Techniques","year":2011,"lang":"en","type":"article","venue":"Journal of Lightwave Technology","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Wavelet; Noise reduction; Computer science; Reduction (mathematics); Time domain; Wavelet transform; Shrinkage; Artificial intelligence; Algorithm; Computer vision; Mathematics; Machine learning","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":[0.0008956126,0.0007767136,0.0009676352,0.0006168531,0.0003865612,0.0005864657,0.0009226081,0.0006594833,0.0009740244],"category_scores_gemma":[0.001880773,0.0004818272,0.0005130282,0.000443143,0.000469301,0.001330999,0.0007330543,0.0009848619,0.0003755207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004011551,"about_ca_system_score_gemma":0.0003028204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004330317,"about_ca_topic_score_gemma":0.001265428,"domain_scores_codex":[0.9990959,0.0001011912,0.00006076975,0.0001963794,0.0004933039,0.00005248496],"domain_scores_gemma":[0.9983223,0.0006851527,0.0002475128,0.0002747585,0.0004105676,0.00005966793],"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":[0.0002348201,0.00007487714,0.0008601905,0.0001556329,0.00003611508,0.0001221548,0.0001359248,0.00400349,0.8807722,0.001378465,0.0004104512,0.1118158],"study_design_scores_gemma":[0.00002033078,0.0004453529,0.003129014,0.00002074381,0.00005072896,0.0004344579,0.00007981044,0.1377509,0.8495779,0.001230805,0.00719733,0.0000625816],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1573294,0.0006628484,0.8392966,0.0002378801,0.0001390486,0.00006841494,0.0000735799,0.000970148,0.0012221],"genre_scores_gemma":[0.3369594,0.0006079974,0.6598467,0.0001277794,0.00006713417,0.0001288747,0.0001713066,0.0002292554,0.001861616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009740244,"threshold_uncertainty_score":0.004736543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02691691396642277,"score_gpt":0.2677968891163371,"score_spread":0.2408799751499143,"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."}}