{"id":"W2036704883","doi":"10.1016/j.measurement.2012.12.001","title":"Enhancement of oil debris sensor capability by reliable debris signature extraction via wavelet domain target and interference signal tracking","year":2012,"lang":"en","type":"article","venue":"Measurement","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Debris; Interference (communication); Signature (topology); Wavelet; Noise (video); SIGNAL (programming language); Debris flow; Computer science; Environmental science; Remote sensing; Geology; Artificial intelligence; Physics; Mathematics; Meteorology; Telecommunications","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":[0.000315606,0.0004599215,0.0003240368,0.0004057437,0.0001530659,0.0003879953,0.0003761289,0.0004232056,0.0006511854],"category_scores_gemma":[0.001110979,0.0002225984,0.0001868078,0.0003558351,0.0001786378,0.000904636,0.0003974207,0.0003095114,0.0003815936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001167462,"about_ca_system_score_gemma":0.0002205006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001767441,"about_ca_topic_score_gemma":0.0003325228,"domain_scores_codex":[0.999761,0.00002649466,0.00001265695,0.00003203424,0.000145669,0.00002212522],"domain_scores_gemma":[0.9992477,0.0002590793,0.0001259688,0.0000877807,0.0002584764,0.00002108141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004452126,0.00007847483,0.002677485,0.0001240268,0.0000197414,0.0001012573,0.0000750213,0.008861193,0.7983739,0.001084582,0.0003662589,0.1877929],"study_design_scores_gemma":[0.00001693268,0.0002505859,0.004461427,0.00001051602,0.00003298326,0.0003362673,0.00002445341,0.1885877,0.8035304,0.0006288192,0.002099531,0.00002039293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1760139,0.0003074334,0.8208719,0.000117629,0.00004798983,0.00002468486,0.00004758464,0.0006952806,0.0018736],"genre_scores_gemma":[0.8931349,0.0002088013,0.1047442,0.00006296994,0.00003371888,0.00002280633,0.00007912313,0.0000592888,0.001654101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0006511854,"threshold_uncertainty_score":0.002178431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01619786387055704,"score_gpt":0.25047883928076,"score_spread":0.2342809754102029,"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."}}