{"id":"W4394686310","doi":"10.20944/preprints202403.1198.v2","title":"Monitoring the Wear Trend in Wind Turbines by Tracking the Fourier Vibration Spectrum and Base Density Support Vector Machine","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Vibration; Base (topology); Tracking (education); Wind power; Spectral density; Spectrum (functional analysis); Acoustics; Fourier transform; Computer science; Structural engineering; Engineering; Physics; Mathematics; Telecommunications; Electrical engineering; Psychology; Mathematical analysis","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":[0.000325714,0.0002920386,0.0002219592,0.0008915705,0.0001053127,0.00031994,0.0002703732,0.0003216462,0.0003917642],"category_scores_gemma":[0.001662638,0.0001391833,0.0001167178,0.0004676132,0.0002206731,0.0006015779,0.0001962304,0.0002681314,0.0001260687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001077561,"about_ca_system_score_gemma":0.00009604212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007164785,"about_ca_topic_score_gemma":0.0008767144,"domain_scores_codex":[0.9998222,0.00003065333,0.000009601866,0.00003467553,0.00009205066,0.00001081991],"domain_scores_gemma":[0.9995096,0.0002122698,0.0001036052,0.00004023368,0.0001155018,0.00001874977],"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":[0.0002821311,0.0001965251,0.02737061,0.0002342204,0.00007470478,0.0002225513,0.000260766,0.1258671,0.1315294,0.002580271,0.00116955,0.7102121],"study_design_scores_gemma":[0.000005665747,0.00009948947,0.0167404,0.000009905478,0.00001138224,0.0001290041,0.00003943218,0.9657384,0.01560454,0.00117198,0.0004339018,0.00001605464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4404854,0.0003663272,0.5572237,0.00008893053,0.00004180479,0.00002788312,0.0001107111,0.0005601036,0.001095216],"genre_scores_gemma":[0.9377059,0.0001249382,0.06149305,0.00000965902,0.00001768806,0.0000113288,0.00008367532,0.00002528624,0.0005285317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008915705,"threshold_uncertainty_score":0.001722515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03361650764975996,"score_gpt":0.2684485321789636,"score_spread":0.2348320245292037,"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."}}