{"id":"W2989515309","doi":"10.12783/shm2019/32195","title":"Defect Sizing Using Convolution Neural Network Applied to Guided Wave Imaging","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Sizing; Fuselage; Convolutional neural network; Convolution (computer science); Computer science; Structural health monitoring; Pixel; Aerospace; Nondestructive testing; Actuator; Materials science; Acoustics; Artificial neural network; Structural engineering; Artificial intelligence; 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":[0.0003161875,0.0003861658,0.0003756263,0.0006556445,0.0001199507,0.0004330596,0.0005784122,0.0005164362,0.0007855375],"category_scores_gemma":[0.0008347744,0.0002239496,0.0003593135,0.0003836414,0.0002323279,0.0004082322,0.0003556527,0.0003218683,0.0001542511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000545581,"about_ca_system_score_gemma":0.0004106604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005114714,"about_ca_topic_score_gemma":0.005178025,"domain_scores_codex":[0.9998807,0.00001262516,0.000008557044,0.00003380232,0.00004441532,0.00001979791],"domain_scores_gemma":[0.9997062,0.00009174316,0.00004848002,0.00003322784,0.0001047877,0.00001558139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002708036,0.000109528,0.004938934,0.00009002221,0.00006232999,0.000151492,0.00006930321,0.5503605,0.04745078,0.002070025,0.001118576,0.3933077],"study_design_scores_gemma":[0.000001023822,0.00001034555,0.0004021004,0.000001408997,0.000003088275,0.00001313735,0.000002383473,0.996241,0.003094642,0.0001439347,0.00008492555,0.000002001508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1701271,0.0002320171,0.8266549,0.0001200184,0.00003892856,0.00005259174,0.0001309468,0.001052992,0.001590605],"genre_scores_gemma":[0.8375199,0.0001408412,0.1599046,0.00005077218,0.00001529998,0.00005275782,0.0003158184,0.00004259387,0.001957509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005114714,"threshold_uncertainty_score":0.01016986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02629752047838996,"score_gpt":0.2333593347298807,"score_spread":0.2070618142514908,"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."}}