{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004876142,0.00006649452,0.00006920497,0.00003563627,0.00001473853,0.00001442718,0.00008125516,0.00004678264,0.0001449342],"category_scores_gemma":[0.00001249794,0.00005972218,0.00001654685,0.00004455858,0.000006235987,0.00005810407,0.00001369725,0.00007912592,0.0001174498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002169601,"about_ca_system_score_gemma":0.0000013402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002203916,"about_ca_topic_score_gemma":0.00002605355,"domain_scores_codex":[0.9996619,0.00001262353,0.00008524676,0.00007842582,0.00006254042,0.00009927859],"domain_scores_gemma":[0.9997607,0.00004097262,0.000004880347,0.0001433442,0.00001092213,0.00003910937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001154682,0.00020375,0.03422175,0.0001685229,0.00004906656,0.00000450112,0.0002614519,0.0230282,0.06679411,0.07032506,0.6401293,0.1648027],"study_design_scores_gemma":[0.0001204113,0.00004819886,0.01069448,0.00001725955,0.000005745624,0.000004426079,0.000001846875,0.8285325,0.09108526,0.00589835,0.06334896,0.0002425538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3325496,0.00001381277,0.3521934,0.0002060669,0.00005977938,0.00007926912,0.000002163201,0.002615448,0.3122804],"genre_scores_gemma":[0.9799961,0.00001206774,0.01965136,0.0002171098,0.00003480923,0.00002166208,0.000009704276,0.00001264136,0.00004449196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8055043,"threshold_uncertainty_score":0.24354,"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."}}