{"id":"W4411665802","doi":"10.1016/j.geits.2025.100332","title":"Toward smart railway maintenance: AI-enhanced Non-Destructive Evaluation using Vision Transformers and CNNs for fastener defect detection","year":2025,"lang":"en","type":"article","venue":"Green Energy and Intelligent Transportation","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; Centre intégré de santé et de services sociaux de Chaudière-Appalaches; Université du Québec à Rimouski","funders":"Science and Engineering Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Deep learning","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":[0.0005174991,0.0008481122,0.0003397982,0.0008067092,0.0001338996,0.0008474039,0.0007932281,0.0006419619,0.001281516],"category_scores_gemma":[0.001320178,0.00017114,0.0003726953,0.0004400994,0.0002333527,0.001097282,0.0004691755,0.0005100041,0.0004974187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007645553,"about_ca_system_score_gemma":0.000554929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008073745,"about_ca_topic_score_gemma":0.01222912,"domain_scores_codex":[0.9998273,0.0000232098,0.000007794496,0.00005208141,0.00005605164,0.00003359743],"domain_scores_gemma":[0.999733,0.0000690822,0.00004135201,0.00003885062,0.00009829689,0.00001931248],"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":[0.0004208286,0.0003166103,0.01717868,0.0001884306,0.0001514026,0.000208537,0.000081554,0.3278155,0.03581122,0.002688475,0.00617223,0.6089665],"study_design_scores_gemma":[0.000006795645,0.0001138271,0.002627438,0.00001244943,0.00002885775,0.00005576892,0.00002658493,0.985929,0.009597923,0.000742427,0.0008512882,0.000007634868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4864405,0.001921048,0.4908043,0.0006182467,0.0002492412,0.0001682377,0.0008708608,0.00731048,0.01161703],"genre_scores_gemma":[0.9659963,0.0002465962,0.02967239,0.00009712327,0.00002020201,0.00002276113,0.0005964767,0.00005239178,0.003295868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008073745,"threshold_uncertainty_score":0.0160535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01042490137910379,"score_gpt":0.2506945964137462,"score_spread":0.2402696950346424,"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."}}