{"id":"W2537100124","doi":"10.1109/rose.2005.1588338","title":"De-noising mechanical signals by hybrid thresholding","year":2006,"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":"Thresholding; Pattern recognition (psychology); Artificial intelligence; Balanced histogram thresholding; Computer science; Mean squared error; Noise reduction; Wavelet; Offset (computer science); Feature extraction; Additive white Gaussian noise; Noise (video); Gaussian noise; White noise; Mathematics; Statistics; Histogram","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":[0.0007636746,0.0006220019,0.0006744611,0.0007761931,0.0002082833,0.0006968981,0.0006022399,0.0007273812,0.000623643],"category_scores_gemma":[0.002216212,0.0002278692,0.0003835219,0.0005746286,0.0005769927,0.0009780821,0.0006726822,0.0004966167,0.0002856402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002019828,"about_ca_system_score_gemma":0.0001535445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002802935,"about_ca_topic_score_gemma":0.0006809053,"domain_scores_codex":[0.999382,0.00009020633,0.00004106934,0.00009774572,0.0003521026,0.00003689875],"domain_scores_gemma":[0.9991542,0.0004179326,0.0001013384,0.0001024717,0.0002024965,0.00002154953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003337292,0.00007365322,0.001259842,0.0002760928,0.00007203616,0.0001852663,0.0001438283,0.04587152,0.4715002,0.004603012,0.0005491648,0.4751317],"study_design_scores_gemma":[0.00003075616,0.0004422891,0.003261856,0.0000413792,0.00007717482,0.0005943806,0.00007389878,0.68718,0.2987605,0.004434136,0.005031766,0.0000718092],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04737573,0.0004194394,0.9506741,0.00007020661,0.00006852007,0.00002845314,0.00001785109,0.0002622305,0.001083465],"genre_scores_gemma":[0.4369404,0.0004984771,0.5598319,0.0001099204,0.00008831748,0.00005524196,0.00008114437,0.00009528655,0.002299196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007761931,"threshold_uncertainty_score":0.004038692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005000201017163173,"score_gpt":0.2383819221097402,"score_spread":0.233381721092577,"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."}}