{"id":"W4318615770","doi":"10.1016/j.ymssp.2023.110131","title":"On the estimation of the evolutionary power spectral density","year":2023,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Power Quality and Harmonics","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Short-time Fourier transform; Mathematics; Residual; Fourier transform; Smoothness; Wavelet transform; Discrete wavelet transform; Spectral density estimation; Harmonic wavelet transform; Spectral density; Continuous wavelet transform; Discrete Fourier transform (general); Applied mathematics; Wavelet; Algorithm; Context (archaeology); Probability density function; Mathematical optimization; Mathematical analysis; Computer science; Artificial intelligence; Statistics; Fourier analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002242727,0.0006768381,0.0007766677,0.001082181,0.0002799829,0.000982467,0.0009940275,0.001023061,0.001438679],"category_scores_gemma":[0.01629692,0.0004309408,0.0005458835,0.0007992937,0.0009228864,0.001426019,0.00115538,0.0009976602,0.0005020138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003860434,"about_ca_system_score_gemma":0.0005185434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003350198,"about_ca_topic_score_gemma":0.001859036,"domain_scores_codex":[0.9992312,0.0004077177,0.00003035433,0.0001221438,0.0001685975,0.00003998859],"domain_scores_gemma":[0.9941527,0.005004589,0.0001716417,0.0001950779,0.0004104501,0.00006560584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001321421,0.00006404427,0.002221387,0.0001697086,0.0001023339,0.0001084854,0.0001810927,0.6405162,0.005921392,0.05114888,0.001391415,0.2980429],"study_design_scores_gemma":[0.000003009916,0.00001005139,0.0003692071,0.000009199872,0.000004376263,0.00003089977,0.000007012897,0.9930397,0.0004956363,0.005662082,0.0003626285,0.000006124936],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007571534,0.0002594723,0.9914408,0.000080242,0.00001996757,0.000010874,0.00001352873,0.00005489895,0.0005487746],"genre_scores_gemma":[0.4641359,0.001736623,0.5280675,0.0001614358,0.0002845761,0.00009814752,0.000364045,0.0001681838,0.004983727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003350198,"threshold_uncertainty_score":0.01186085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02325827894323784,"score_gpt":0.2296081459782253,"score_spread":0.2063498670349875,"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."}}