{"id":"W4406330302","doi":"10.3390/machines13010049","title":"Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer Models","year":2025,"lang":"en","type":"article","venue":"Machines","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Trois-Rivières","keywords":"Transformer; Computer science; Hyperparameter; Artificial intelligence; Transfer of learning; Machine learning; Deep learning; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003309002,0.002296149,0.0009290277,0.001866617,0.0003066977,0.001165748,0.001981076,0.002129957,0.001513528],"category_scores_gemma":[0.00678324,0.0002706517,0.0009594684,0.001080943,0.00087223,0.002273253,0.001280941,0.001403957,0.001154791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001641162,"about_ca_system_score_gemma":0.0008872455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0122837,"about_ca_topic_score_gemma":0.008818062,"domain_scores_codex":[0.999051,0.0001795425,0.00006240516,0.0003097926,0.0002604571,0.0001367293],"domain_scores_gemma":[0.9982609,0.0007871884,0.0001480334,0.0003008834,0.0003790476,0.0001239644],"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":[0.00127008,0.0007955859,0.01871108,0.0003303885,0.000371635,0.0002131929,0.00007296533,0.6771787,0.006987058,0.001081284,0.0083214,0.2846666],"study_design_scores_gemma":[0.00003313976,0.0002864593,0.001855658,0.00002046628,0.00003623473,0.00007716368,0.00003532932,0.9907296,0.005726255,0.0007233157,0.0004645415,0.00001173964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8815638,0.004010644,0.09335119,0.001000063,0.0004406095,0.0002640441,0.002226758,0.01120839,0.005934421],"genre_scores_gemma":[0.9700734,0.0003732641,0.02302509,0.0002672281,0.00003836262,0.00004513592,0.003784751,0.0001838637,0.002208863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0122837,"threshold_uncertainty_score":0.02442443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02562686478956151,"score_gpt":0.2786410286262436,"score_spread":0.2530141638366821,"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."}}