{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003934985,0.00006921017,0.0001019234,0.0000183233,0.0001618927,0.00003505957,0.0000886952,0.00005334784,0.000009197452],"category_scores_gemma":[0.00003166375,0.00003915029,0.00003454426,0.000178278,0.00002665171,0.00005277984,0.00002769921,0.0001395941,0.000009312724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002382258,"about_ca_system_score_gemma":0.00001431024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009452968,"about_ca_topic_score_gemma":0.000001177611,"domain_scores_codex":[0.9993864,0.0000485308,0.0001669319,0.00008342692,0.0001906118,0.0001240666],"domain_scores_gemma":[0.9997275,0.0001061272,0.00003405611,0.0000862792,0.00001981608,0.00002620271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007293818,0.00009395825,0.0001559781,0.001603941,0.0001211948,0.0000131464,0.002478494,0.2645499,0.05970982,0.6529385,0.005775737,0.01248637],"study_design_scores_gemma":[0.0000730909,0.00002344768,0.001212935,0.0002005032,0.000008988809,0.000007561335,0.0001584263,0.9828015,0.002658395,0.0126463,0.0001351927,0.00007363213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9672198,0.0004477893,0.03069968,0.0003831311,0.0002679442,0.0001838329,0.000006574832,0.0001522939,0.0006388937],"genre_scores_gemma":[0.9998582,0.000004577379,0.00002615115,0.0000297718,0.00002713045,0.000005420793,9.248596e-7,0.000007882905,0.00003991837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7182516,"threshold_uncertainty_score":0.1596503,"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."}}