{"id":"W4405360632","doi":"10.1115/ipc2024-134124","title":"A Comparison of Wavelet Transform-Based and Fourier Transform-Based Denoising Methods for Strain-Based Pipeline Dent Assessments","year":2024,"lang":"en","type":"article","venue":"","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Wavelet transform; Fourier transform; Pipeline (software); Harmonic wavelet transform; Computer science; Noise reduction; Artificial intelligence; Wavelet; Discrete wavelet transform; Pattern recognition (psychology); Computer vision; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002754468,0.000951551,0.0007537244,0.001651051,0.0002663697,0.0008249348,0.0008424039,0.001263242,0.000914118],"category_scores_gemma":[0.006184409,0.0003572364,0.0008912737,0.0007163535,0.0004246083,0.00123076,0.0007334191,0.0008042587,0.0004220084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003225041,"about_ca_system_score_gemma":0.0005669711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002702559,"about_ca_topic_score_gemma":0.003067522,"domain_scores_codex":[0.999092,0.0001788111,0.00007638184,0.0001374851,0.0004620181,0.00005333461],"domain_scores_gemma":[0.9973322,0.001028725,0.0002610588,0.0002290004,0.001081991,0.00006699206],"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":[0.001209801,0.0004145427,0.007688406,0.0006658265,0.0002283851,0.0002308051,0.000414096,0.2016391,0.1032069,0.00297906,0.001271361,0.6800517],"study_design_scores_gemma":[0.00002324309,0.0001630045,0.00459469,0.000042031,0.00005592198,0.0001385839,0.00007941017,0.9700725,0.02346034,0.0004494315,0.0008811235,0.00003970416],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1429543,0.001086505,0.8528027,0.0001674488,0.0001015111,0.00008351092,0.00009088829,0.0006040151,0.002109124],"genre_scores_gemma":[0.5125535,0.001287138,0.4832101,0.0000926821,0.00005976222,0.00008529551,0.0003288885,0.0001645049,0.002218097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002754468,"threshold_uncertainty_score":0.0145672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04091230167660892,"score_gpt":0.4012283794046387,"score_spread":0.3603160777280298,"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."}}