{"id":"W4403137537","doi":"10.3390/ndt2040023","title":"Automated Defect Detection through Flaw Grading in Non-Destructive Testing Digital X-ray Radiography","year":2024,"lang":"en","type":"article","venue":"NDT","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Digital radiography; Nondestructive testing; Grading (engineering); Radiographic testing; Radiography; Medical physics; Computer science; Materials science; Engineering; Radiology; Medicine; Mechanical 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":[0.001176178,0.0005391088,0.0005729825,0.001728174,0.0001813031,0.001015914,0.00124112,0.0007384838,0.000677839],"category_scores_gemma":[0.003294678,0.0003594797,0.0004723767,0.000659455,0.0005693714,0.0007374795,0.0007041311,0.000429355,0.0005376516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004704881,"about_ca_system_score_gemma":0.0005235171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001421021,"about_ca_topic_score_gemma":0.002103248,"domain_scores_codex":[0.9985698,0.0002211466,0.00009219033,0.0002365681,0.0007874207,0.00009288309],"domain_scores_gemma":[0.9981822,0.0006309808,0.0003013786,0.0002301072,0.0005865147,0.00006878053],"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":[0.0002341421,0.0002103001,0.005393843,0.0003169017,0.00003291558,0.0002181709,0.0002002492,0.05131077,0.2814867,0.003037655,0.001240864,0.6563174],"study_design_scores_gemma":[0.00002806649,0.0002929802,0.007117326,0.00003800222,0.0000348369,0.0004644881,0.00005018011,0.8345615,0.1526071,0.00222771,0.002528902,0.00004881909],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07535363,0.0002796355,0.9208195,0.00005551829,0.00003209281,0.0001551616,0.00005284795,0.002086597,0.001165033],"genre_scores_gemma":[0.4639226,0.0002431957,0.5338792,0.000062007,0.00001536475,0.00007996244,0.0001295616,0.00008578438,0.001582258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001728174,"threshold_uncertainty_score":0.006220281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007462285022341255,"score_gpt":0.2259118000160316,"score_spread":0.2184495149936904,"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."}}