{"id":"W2885948806","doi":"10.3390/met8080612","title":"Pattern Deep Region Learning for Crack Detection in Thermography Diagnosis System","year":2018,"lang":"en","type":"article","venue":"Metals","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Engineering and Physical Sciences Research Council; National Natural Science Foundation of China","keywords":"Thermography; Deep learning; Artificial intelligence; Artificial neural network; Computer science; Pattern recognition (psychology); Eddy-current testing; Convolution (computer science); Convolutional neural network; Range (aeronautics); Machine learning; Engineering; Eddy current; Infrared; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002248327,0.0001236569,0.0001655222,0.0002275053,0.00006085884,0.00002367559,0.0000878041,0.00009647502,0.00001229822],"category_scores_gemma":[0.00001677297,0.0001186889,0.0001187176,0.0002728027,0.0000274147,0.00008122914,0.000008299112,0.0001069536,0.000003811206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003012081,"about_ca_system_score_gemma":0.000001247402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004204736,"about_ca_topic_score_gemma":0.0001136536,"domain_scores_codex":[0.9993623,0.00004794444,0.0001860659,0.0001439732,0.00006538853,0.0001943332],"domain_scores_gemma":[0.9996669,0.0000938009,0.00003732954,0.0001414691,0.00003060325,0.00002992903],"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":[0.00005484796,0.00005937558,0.01908454,0.000811113,0.0002373855,0.00001011413,0.002153423,0.003051303,0.06915292,0.00009833981,0.000305509,0.9049811],"study_design_scores_gemma":[0.0007741116,0.0005048682,0.02684085,0.000447703,0.000164824,0.00002455069,0.0005684305,0.2015058,0.7533884,0.0006201367,0.01438992,0.0007703971],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.520893,0.0003554397,0.4771436,0.00000576406,0.0002010552,0.0003276655,0.000006410737,0.0006945495,0.0003724982],"genre_scores_gemma":[0.9989834,0.00004427663,0.0002724761,0.0000147151,0.0001495331,0.0004872972,0.000002822898,0.0000373719,0.000008148174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9042107,"threshold_uncertainty_score":0.4839992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01191375600335949,"score_gpt":0.2165921694979258,"score_spread":0.2046784134945664,"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."}}