{"id":"W4405024469","doi":"10.3390/ndt2040032","title":"Advanced Defect Detection on Curved Aeronautical Surfaces Through Infrared Imaging and Deep Learning","year":2024,"lang":"en","type":"article","venue":"NDT","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski; Université Laval","funders":"Agency for Science, Technology and Research","keywords":"Aerospace; Robustness (evolution); Deep learning; Computer science; Artificial intelligence; Segmentation; Computer vision; Machine learning; Reliability engineering; Engineering; Aerospace engineering","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":[0.0004131816,0.0008025802,0.000439388,0.001314598,0.0001567635,0.0006175534,0.0008512581,0.000746238,0.0006358693],"category_scores_gemma":[0.001114427,0.0002810415,0.0004553545,0.0004930334,0.0005437692,0.001052097,0.0008002971,0.000736604,0.0003274416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000464383,"about_ca_system_score_gemma":0.0003350776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002047237,"about_ca_topic_score_gemma":0.003612914,"domain_scores_codex":[0.9997328,0.00003276579,0.000009885429,0.00006981588,0.0001209259,0.00003377576],"domain_scores_gemma":[0.9994319,0.0001462197,0.0001227696,0.00007816462,0.0001869376,0.00003399987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002488466,0.0002653577,0.01280791,0.0002316711,0.00009170779,0.0003360026,0.0001951119,0.2721838,0.147837,0.003214582,0.003498277,0.5590898],"study_design_scores_gemma":[0.000003448889,0.00005157155,0.00224019,0.00001169197,0.00001021272,0.0001039668,0.00002637984,0.9792556,0.01615038,0.001521535,0.0006148105,0.0000102941],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1646638,0.0006658441,0.830117,0.0002664388,0.00005448397,0.00005116148,0.0001588834,0.001890217,0.002132193],"genre_scores_gemma":[0.7980271,0.0004249745,0.1985242,0.000164007,0.00003499126,0.00003475476,0.00034182,0.0001137283,0.002334348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002047237,"threshold_uncertainty_score":0.00407064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01111624813963371,"score_gpt":0.240973621254154,"score_spread":0.2298573731145203,"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."}}