{"id":"W4367181929","doi":"10.3390/s23094324","title":"Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters—An Experimental Study","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Artificial intelligence; Nondestructive testing; Deep learning; Image quality; Digital radiography; Computer science; Artificial neural network; Noise (video); Radiography; Pattern recognition (psychology); Machine learning; Image (mathematics); Medicine","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":[0.001257438,0.0008912312,0.0004878278,0.0006029831,0.0002291064,0.0005683649,0.0006350577,0.0009735841,0.001051356],"category_scores_gemma":[0.003805163,0.0002421954,0.000369204,0.000445769,0.0005402901,0.0006435611,0.0005910621,0.0006443313,0.0002631656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006878587,"about_ca_system_score_gemma":0.000445664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006982144,"about_ca_topic_score_gemma":0.004713432,"domain_scores_codex":[0.9996288,0.00006434353,0.00003427399,0.00008272856,0.0001111972,0.00007866727],"domain_scores_gemma":[0.9986727,0.0005802882,0.0001374402,0.0001089517,0.0004265375,0.0000741057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003490533,0.001705287,0.01523796,0.0008737952,0.000288166,0.0004522429,0.0003025019,0.5887787,0.1191687,0.001255732,0.002437814,0.2660087],"study_design_scores_gemma":[0.00003146458,0.0007956215,0.006925128,0.00004507644,0.00004918466,0.00007970336,0.00007211017,0.9448938,0.04635596,0.000315192,0.000413766,0.00002290964],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9810572,0.0006136678,0.01571609,0.0001596083,0.00005493142,0.00004380659,0.0002121445,0.0003900673,0.001752414],"genre_scores_gemma":[0.9864893,0.0002456176,0.01151609,0.00005959628,0.000008375669,0.00003548013,0.0004456119,0.00003527789,0.001164668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006982144,"threshold_uncertainty_score":0.01388299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01746647236080209,"score_gpt":0.2959973123200526,"score_spread":0.2785308399592505,"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."}}