{"id":"W2172168632","doi":"10.1109/acc.2008.4586919","title":"Enhancement of the signals collected by oil debris sensors","year":2008,"lang":"en","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Noise (video); SIGNAL (programming language); Vibration; Condition monitoring; Oil analysis; Computer science; Wavelet; Materials science; Acoustics; Automotive engineering; Environmental science; Engineering; Petroleum engineering; Artificial intelligence; Electrical engineering; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002690204,0.0004296281,0.0003150832,0.0004170374,0.0001232013,0.000309228,0.000293893,0.0004145284,0.0005539556],"category_scores_gemma":[0.001166452,0.00011567,0.0001676331,0.0003095055,0.0001735825,0.0004025537,0.0003126972,0.0003259681,0.0002876546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000136355,"about_ca_system_score_gemma":0.0001538253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002681498,"about_ca_topic_score_gemma":0.0002866633,"domain_scores_codex":[0.999696,0.00004163471,0.0000147287,0.00004647802,0.0001726544,0.0000284662],"domain_scores_gemma":[0.9994885,0.0001305793,0.00006293398,0.00004916668,0.0002472498,0.00002166163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006637268,0.00009225973,0.003713578,0.0002182273,0.00002194242,0.000268144,0.000209111,0.007853049,0.8104346,0.0004560957,0.0005388897,0.1755304],"study_design_scores_gemma":[0.0000336298,0.0006952733,0.01892888,0.00003978375,0.00004915519,0.0006403518,0.0001163403,0.09849945,0.8741254,0.0003346023,0.006493751,0.00004328802],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6358045,0.0005656242,0.3586145,0.0001974208,0.0001106757,0.0001116674,0.0001861898,0.0009863486,0.003423057],"genre_scores_gemma":[0.8864837,0.0005070483,0.1093656,0.0001164365,0.00006244767,0.00006578267,0.0002518805,0.00006698118,0.003080222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005539556,"threshold_uncertainty_score":0.001853228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007556902687587653,"score_gpt":0.2289002705707467,"score_spread":0.221343367883159,"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."}}