{"id":"W7104045598","doi":"10.1109/tii.2025.3624577","title":"Retrospective Prototype Network Based on Center Difference Measure for Cross-Machine Few-Shot Fault Diagnosis","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Covariance matrix; Generalization; Fault (geology); Measure (data warehouse); Adaptability; Similarity measure; Similarity (geometry); Covariance","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001408802,0.000834076,0.0008803707,0.0006432929,0.001933899,0.001553779,0.001252894,0.0009474785,0.0002454402],"category_scores_gemma":[0.0003328287,0.0008176138,0.0006084413,0.002252064,0.0003011147,0.0009365124,0.00001451975,0.002556344,0.00009919694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007816644,"about_ca_system_score_gemma":0.001009353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004208333,"about_ca_topic_score_gemma":0.00005215243,"domain_scores_codex":[0.9947304,0.0003403879,0.001953758,0.0006679966,0.001164113,0.001143311],"domain_scores_gemma":[0.9957652,0.00111177,0.0007926603,0.001162478,0.0007972395,0.0003706186],"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":[0.003170892,0.001327951,0.002288097,0.0002131201,0.0003644209,0.000003031613,0.002312036,0.7859858,0.000004867277,0.002543679,0.002635538,0.1991506],"study_design_scores_gemma":[0.009862642,0.002592609,0.0007617314,0.00159318,0.0001695664,0.000002435347,0.0001750169,0.96551,0.001211816,0.0003126135,0.01695386,0.0008545025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00169966,0.00002665163,0.977575,0.001240465,0.008076271,0.005064837,0.0004765864,0.000272147,0.005568406],"genre_scores_gemma":[0.9877036,0.00004598157,0.005985979,0.002559528,0.0003927633,0.001668966,0.0000351703,0.00004979056,0.001558276],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9860039,"threshold_uncertainty_score":0.9997448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06593037772478041,"score_gpt":0.3084342763249937,"score_spread":0.2425038986002133,"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."}}