{"id":"W4412352918","doi":"10.1109/ddcls66240.2025.11064990","title":"The Fault Diagnosis Model for Variable Speed Rolling Bearings Based on GCRA-FMD, COT, and Deep Convolutional Neural Networks","year":2025,"lang":"en","type":"article","venue":"","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Variable (mathematics); Computer science; Artificial intelligence; Fault (geology); Artificial neural network; Deep learning; Pattern recognition (psychology); Geology; Mathematics; Seismology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002325471,0.0005732949,0.0003758936,0.0004261707,0.000236139,0.0003735783,0.0006973748,0.0004695947,0.0009785416],"category_scores_gemma":[0.0004206745,0.000240208,0.0003733497,0.0002474871,0.0002463524,0.0004850293,0.0002973863,0.0006487009,0.0001923969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007968158,"about_ca_system_score_gemma":0.0007747222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01992201,"about_ca_topic_score_gemma":0.02203344,"domain_scores_codex":[0.9999114,0.000007747813,0.000005610093,0.00003123423,0.00003018645,0.00001381108],"domain_scores_gemma":[0.9998649,0.00003098792,0.00002544594,0.00001146267,0.00005845267,0.000008788985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001440154,0.0000660884,0.003111146,0.00008297284,0.00004514477,0.0001194408,0.0000515393,0.8487572,0.01809209,0.002785223,0.001076093,0.125669],"study_design_scores_gemma":[0.000001441494,0.00001361352,0.0002479191,0.000001699303,0.000003964329,0.00001250968,0.000001363743,0.9985899,0.0008069038,0.0001805027,0.0001380063,0.000002103107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09014639,0.0007188239,0.9042367,0.0003615432,0.0001231871,0.0000636424,0.0001785857,0.001121204,0.003049964],"genre_scores_gemma":[0.9506097,0.0002814243,0.04494865,0.00006402421,0.00002226597,0.00005755718,0.0001652946,0.00002139964,0.003829673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01992201,"threshold_uncertainty_score":0.03961211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00574519837252442,"score_gpt":0.2000598653128355,"score_spread":0.1943146669403111,"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."}}