{"id":"W2180187379","doi":"10.3414/me14-02-0024","title":"Assignment of Empirical Mode Decomposition Components and Its Application to Biomedical Signals","year":2015,"lang":"en","type":"article","venue":"Methods of Information in Medicine","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Cardinality (data modeling); Pairwise comparison; Hilbert–Huang transform; Algorithm; SIGNAL (programming language); Matching (statistics); Set (abstract data type); Mathematics; Pattern recognition (psychology); Signal processing; Computer science; Artificial intelligence; Data mining; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001533751,0.0007730909,0.0004324857,0.001120874,0.0003883716,0.0006780102,0.000655962,0.00075965,0.002876621],"category_scores_gemma":[0.006024186,0.0002722029,0.0006783656,0.001478903,0.0007301891,0.0007737812,0.001078876,0.0009612587,0.0009082388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004118304,"about_ca_system_score_gemma":0.0005500296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0012696,"about_ca_topic_score_gemma":0.0005708027,"domain_scores_codex":[0.9991707,0.0003079735,0.00004496812,0.0001938911,0.00024849,0.00003397539],"domain_scores_gemma":[0.9985573,0.0007796861,0.0001584881,0.0001530872,0.0003032691,0.00004824186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001996556,0.0001773295,0.002029109,0.0004442753,0.0001056503,0.0001979888,0.0002917719,0.3097917,0.02299117,0.0347274,0.001851891,0.6271921],"study_design_scores_gemma":[0.00001329118,0.00006634249,0.001313896,0.00003420583,0.00001730289,0.0001632689,0.00004410634,0.9713513,0.005363589,0.01782351,0.003787226,0.0000219076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007441007,0.000478029,0.9909192,0.00007517257,0.00003520543,0.00003363557,0.0000243364,0.0001646767,0.0008287136],"genre_scores_gemma":[0.2706164,0.001320505,0.7255694,0.00004243998,0.00009612708,0.0001947244,0.0001738329,0.0001311283,0.001855321],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002876621,"threshold_uncertainty_score":0.00962323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04721678712148138,"score_gpt":0.4882607123002048,"score_spread":0.4410439251787234,"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."}}