{"id":"W4409360089","doi":"10.1139/tcsme-2024-0167","title":"A tractor transmission systems vibration fault data augmentation and diagnosis method based on DCGAN","year":2025,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tractor; Vibration; Fault (geology); Transmission (telecommunications); Structural engineering; Automotive engineering; Computer science; Engineering; Control theory (sociology); Acoustics; Physics; Artificial intelligence; Geology; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0004744469,0.001069955,0.0006222925,0.0008081695,0.000286333,0.0003965427,0.0008874598,0.0007095672,0.001472249],"category_scores_gemma":[0.000974319,0.0003445333,0.000709875,0.0004272897,0.0004056884,0.0008430949,0.001042331,0.001211433,0.0005967594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005154101,"about_ca_system_score_gemma":0.0007907901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004331934,"about_ca_topic_score_gemma":0.005704344,"domain_scores_codex":[0.9996812,0.00003418717,0.00001650437,0.0001106553,0.0001104087,0.00004720128],"domain_scores_gemma":[0.9996614,0.00008195204,0.00004504249,0.00007594752,0.0001146575,0.000020949],"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":[0.0003295857,0.0001723019,0.003487596,0.0001652942,0.000109012,0.0003660348,0.0001249185,0.2672686,0.04969972,0.004352579,0.006627928,0.6672965],"study_design_scores_gemma":[0.000008257605,0.00005071269,0.0007310708,0.000008942969,0.00001511478,0.0001240243,0.00001177221,0.9829002,0.01364513,0.0009374574,0.001554668,0.0000126672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02464541,0.0003049601,0.9696777,0.0002808366,0.0001104841,0.0001209498,0.0002438453,0.002536291,0.002079623],"genre_scores_gemma":[0.6090592,0.0003988884,0.3791606,0.0004300899,0.00009857582,0.000224861,0.001801696,0.00016798,0.00865806],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004331934,"threshold_uncertainty_score":0.008613467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0153980737523248,"score_gpt":0.2600543258253232,"score_spread":0.2446562520729984,"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."}}