{"id":"W2144523750","doi":"10.1109/isic.2002.1157790","title":"Informative wavelet algorithm in diesel engine diagnosis","year":2003,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Diesel engine; Feature extraction; Computer science; Pattern recognition (psychology); Transient (computer programming); Fault (geology); Wavelet transform; Artificial intelligence; Internal combustion engine; Vibration; Wavelet packet decomposition; Feature (linguistics); Automotive engineering; Engineering; Acoustics","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":[0.001531112,0.0004688158,0.000614392,0.001512138,0.0002449228,0.001015965,0.0006918365,0.0009748925,0.0009483344],"category_scores_gemma":[0.003830141,0.0002637317,0.0003142042,0.00140543,0.0005483514,0.001061805,0.0005537751,0.0009988863,0.0006017548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000334712,"about_ca_system_score_gemma":0.0004874646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008836528,"about_ca_topic_score_gemma":0.0005231104,"domain_scores_codex":[0.9994998,0.0001717659,0.00003384478,0.00006281825,0.0001998167,0.00003190473],"domain_scores_gemma":[0.9991859,0.0004482782,0.0000641188,0.0000723917,0.0001913664,0.00003801048],"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":[0.0002726596,0.00009241306,0.001129829,0.0001596082,0.0000509341,0.0002024386,0.0001128528,0.2450082,0.01428434,0.1084583,0.003467541,0.6267609],"study_design_scores_gemma":[0.000013115,0.00004394555,0.0004405615,0.00001384707,0.00001206987,0.00006711866,0.0000143759,0.9690672,0.002890727,0.02536489,0.002061218,0.00001104326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0106581,0.001000818,0.9868116,0.0001394023,0.00004848737,0.00001534259,0.00002707486,0.0001391826,0.00116009],"genre_scores_gemma":[0.3409258,0.003048477,0.6490473,0.0001644828,0.0002588501,0.00009299689,0.000279456,0.00009661049,0.006086034],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001531112,"threshold_uncertainty_score":0.008097351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00488885835512495,"score_gpt":0.1890374685731885,"score_spread":0.1841486102180636,"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."}}