{"id":"W2147483970","doi":"10.1007/3-540-45333-4_46","title":"On the Performance of Informative Wavelets for Classification and Diagnosis of Machine Faults","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wavelet; Computer science; Pattern recognition (psychology); Mutual information; Artificial intelligence; Wavelet packet decomposition; Entropy (arrow of time); Feature extraction; Feature selection; Gabor wavelet; Data mining; Wavelet transform; Discrete wavelet transform","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000238078,0.0001415554,0.0002092197,0.000183212,0.00004918552,0.00001542603,0.0002229398,0.00009247437,0.000004956089],"category_scores_gemma":[0.00003716771,0.00009724208,0.00003720965,0.0001126235,0.0001634647,0.00008154586,0.00002706446,0.0001669194,0.000001175634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004630973,"about_ca_system_score_gemma":0.00001786359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005496628,"about_ca_topic_score_gemma":0.00001735141,"domain_scores_codex":[0.9992654,0.000005483857,0.0002722451,0.0001436755,0.0001989698,0.0001142496],"domain_scores_gemma":[0.9991344,0.0004401951,0.0001220533,0.0002078119,0.0000741017,0.00002150292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004256084,0.00001356263,0.0003364949,0.000454719,0.00003425358,4.982745e-7,0.001543414,0.1548809,0.0006355213,0.005156882,0.00003113756,0.83687],"study_design_scores_gemma":[0.0001719085,0.0001530887,0.0005049652,0.0002843776,0.000004276405,0.000003746057,6.011683e-7,0.9934713,0.003602754,0.001003125,0.0006962166,0.0001036016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0788243,0.0003977069,0.9141085,0.00022121,0.0007577758,0.001001231,0.00004030988,0.00005733647,0.004591644],"genre_scores_gemma":[0.9989557,0.0001192833,0.0007479034,0.00006964956,0.00004062021,0.00002780323,0.000002064635,0.000009422477,0.00002751456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9201314,"threshold_uncertainty_score":0.3965417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0126570520239013,"score_gpt":0.2172379386362104,"score_spread":0.2045808866123091,"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."}}