{"id":"W1916026856","doi":"","title":"Wavelets application in acoustic emission signal detection of wire related events in pipeline","year":2008,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Short-time Fourier transform; Acoustic emission; Wavelet; Wavelet transform; Computer science; SIGNAL (programming language); Time–frequency analysis; Fast wavelet transform; Signal processing; Discrete wavelet transform; Fourier transform; Pipeline (software); Time domain; Acoustics; Pattern recognition (psychology); Electronic engineering; Artificial intelligence; Engineering; Computer vision; Fourier analysis; Mathematics; Digital signal processing; Telecommunications; Radar; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001400439,0.0001318898,0.0001664147,0.0003688259,0.00004322638,0.000002738163,0.00009404871,0.0001861526,0.00001571715],"category_scores_gemma":[0.00008496821,0.0001548758,0.00002468315,0.0005018108,0.00002580399,0.00006595412,0.000006405318,0.0002621721,0.000009678637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005095922,"about_ca_system_score_gemma":0.0001553683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004071835,"about_ca_topic_score_gemma":0.008168261,"domain_scores_codex":[0.9990106,0.00001751679,0.0003939553,0.0001556913,0.0001381997,0.0002840801],"domain_scores_gemma":[0.9995447,0.00004244275,0.00005196797,0.0001423519,0.00006011804,0.0001584526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004241548,0.00001850419,0.002538445,0.00006036109,0.000003720228,0.0000197909,0.0001623532,0.3413161,0.6472552,0.000004005918,0.00006621215,0.008551065],"study_design_scores_gemma":[0.0003990059,0.00001901484,0.06333573,0.00007888282,0.00001185148,0.00002286558,0.00005374261,0.9294793,0.00609054,0.0003135159,0.00002834484,0.0001671995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9110848,0.0001069439,0.08811584,0.00001821226,0.0001341677,0.0002436978,0.00002353999,0.00003936925,0.0002334734],"genre_scores_gemma":[0.9994498,0.0001178717,0.0002346854,0.00001534448,0.00003529369,0.00001483823,0.00004684681,0.00003171639,0.0000535369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6411647,"threshold_uncertainty_score":0.6315654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006018896143409787,"score_gpt":0.1814215555823608,"score_spread":0.175402659438951,"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."}}