{"id":"W4414037619","doi":"10.21203/rs.3.rs-7423034/v1","title":"Fault Feature Extraction for Centrifugal Pump Impellers via EMD and Cyclic Bispectral Slicing","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Natural Science Foundation of Hubei Province; University of Alberta","keywords":"Impeller; Slicing; Centrifugal pump; Fault (geology); Extraction (chemistry); Feature extraction; Computer science; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Engineering; Mechanical engineering; Geology; Chromatography; Chemistry; Computer graphics (images)","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":[0.0002110327,0.0007104821,0.0003567708,0.0009447059,0.0002483617,0.0003804492,0.0003563314,0.0003602476,0.001534909],"category_scores_gemma":[0.0008064593,0.0001799813,0.0003152637,0.0005270264,0.0002808933,0.0006107556,0.0003721976,0.0003770609,0.0002931567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002961598,"about_ca_system_score_gemma":0.0004041383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002024085,"about_ca_topic_score_gemma":0.002132507,"domain_scores_codex":[0.9998441,0.00001512858,0.000009504273,0.00003313147,0.00007490877,0.00002331192],"domain_scores_gemma":[0.9996145,0.00009482489,0.00006812156,0.0000542587,0.0001524032,0.00001584152],"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":[0.000714215,0.00009952193,0.004543251,0.0002436264,0.00004356441,0.0002398257,0.0001186927,0.07203026,0.2299415,0.002714538,0.002575185,0.6867358],"study_design_scores_gemma":[0.00001007157,0.0001055372,0.007189104,0.0000152997,0.0000268246,0.0001949698,0.00005463021,0.8814495,0.1073546,0.001491158,0.00208849,0.00001977952],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.152954,0.0003358396,0.8423341,0.0001839265,0.00005990418,0.00003843767,0.0002926336,0.00173143,0.00206976],"genre_scores_gemma":[0.7979587,0.0001716255,0.1998935,0.00004393309,0.00002379824,0.00002666604,0.0004262711,0.0001121309,0.00134332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002024085,"threshold_uncertainty_score":0.005134761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02564902574036085,"score_gpt":0.406012066278527,"score_spread":0.3803630405381662,"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."}}