{"id":"W4413958527","doi":"10.3390/sym17091438","title":"Dynamic Balance Domain-Adaptive Meta-Learning for Few-Shot Multi-Domain Motor Bearing Fault Diagnosis Under Limited Data","year":2025,"lang":"en","type":"article","venue":"Symmetry","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Domain (mathematical analysis); Bearing (navigation); Dynamic balance; Balance (ability); Fault (geology); Artificial intelligence; Physical medicine and rehabilitation; Medicine; Mathematics; Engineering; Geology","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":[0.001034645,0.001085262,0.001222845,0.0007926513,0.0003470219,0.0006790681,0.001606496,0.00112066,0.001043931],"category_scores_gemma":[0.002879591,0.0004920442,0.0009036995,0.0005526019,0.0006113205,0.001271365,0.00108793,0.001696129,0.0004935158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005620682,"about_ca_system_score_gemma":0.0007065591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002322075,"about_ca_topic_score_gemma":0.003241386,"domain_scores_codex":[0.9996282,0.00008184537,0.00002083315,0.0001453449,0.00007541797,0.00004826865],"domain_scores_gemma":[0.9992133,0.0003765755,0.0001046068,0.0001169228,0.0001317134,0.00005684588],"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":[0.000272009,0.0001499605,0.001897024,0.0001429009,0.0001501023,0.0001831866,0.0001320473,0.6447318,0.01415476,0.00268545,0.003652332,0.3318484],"study_design_scores_gemma":[0.000006078153,0.00003257569,0.0001521333,0.00000642784,0.00000974277,0.00003761713,0.00001088999,0.9951587,0.002009174,0.002226332,0.0003447729,0.000005598538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02334233,0.0007112972,0.9734458,0.0001875364,0.00005665023,0.0000308683,0.00008396812,0.001488557,0.0006529599],"genre_scores_gemma":[0.7447146,0.0003504179,0.2504193,0.0004620508,0.0001061834,0.0001257067,0.0007863468,0.0002178566,0.002817523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002322075,"threshold_uncertainty_score":0.005471826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04150710594761933,"score_gpt":0.3256640539832308,"score_spread":0.2841569480356115,"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."}}