{"id":"W2041320291","doi":"10.1109/ccece.2014.6900938","title":"Reference empirical mode decomposition","year":2014,"lang":"en","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Hilbert–Huang transform; Algorithm; SIGNAL (programming language); Computer science; Set (abstract data type); Decomposition; Pattern recognition (psychology); Wavelet; Representation (politics); Curse of dimensionality; Mode (computer interface); Wavelet transform; Feature (linguistics); Artificial intelligence; Computer vision","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":[0.00102953,0.0008969167,0.0009461679,0.0009203092,0.000264556,0.001319094,0.001158198,0.001123461,0.003600203],"category_scores_gemma":[0.00304329,0.0003102204,0.0007029738,0.001022219,0.0004628939,0.0017313,0.000967414,0.001289505,0.001718849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002468819,"about_ca_system_score_gemma":0.0006118707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006459625,"about_ca_topic_score_gemma":0.0006024785,"domain_scores_codex":[0.9992842,0.0001560366,0.00003662178,0.0001919312,0.0002789014,0.00005239448],"domain_scores_gemma":[0.9992309,0.0001793779,0.00006807665,0.000221555,0.0002673965,0.00003261318],"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":[0.0002560339,0.000088011,0.001635136,0.0003440052,0.0001306479,0.0003090374,0.0001458764,0.1109517,0.05503531,0.1010727,0.007428851,0.7226028],"study_design_scores_gemma":[0.00002521935,0.0001956436,0.001472307,0.00007937123,0.00005049971,0.00066539,0.00006657943,0.9132247,0.01697413,0.02862432,0.03855195,0.00006993421],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002172102,0.0002427276,0.996156,0.00004850308,0.00007898109,0.00001455241,0.00006668481,0.0001796531,0.001040739],"genre_scores_gemma":[0.1157054,0.001132726,0.8765568,0.0002467599,0.0001913796,0.0001166708,0.0009069825,0.0002235925,0.004919624],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003600203,"threshold_uncertainty_score":0.01204389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01901797527916307,"score_gpt":0.3976158310313209,"score_spread":0.3785978557521579,"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."}}