{"id":"W2973648079","doi":"10.1177/1475921719873112","title":"Deep learning for enhancing wavefield image quality in fast non-contact inspections","year":2019,"lang":"en","type":"article","venue":"Structural Health Monitoring","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Università di Bologna","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Grid; Image resolution; Image quality; Data set; Ultrasonic sensor; Computer vision; Artificial neural network; Set (abstract data type); Pattern recognition (psychology); Image (mathematics); Acoustics; Geology","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":[0.001031756,0.0006266796,0.0004986751,0.0007242762,0.0001313236,0.0004142709,0.0006872831,0.0006616946,0.0007160963],"category_scores_gemma":[0.002230668,0.0002586675,0.0003687267,0.000502387,0.0003338509,0.0008065024,0.0006825374,0.0005781502,0.0001794195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004257281,"about_ca_system_score_gemma":0.000434389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002993395,"about_ca_topic_score_gemma":0.003863175,"domain_scores_codex":[0.9996692,0.00005904001,0.00001778231,0.00006012879,0.0001380995,0.00005573205],"domain_scores_gemma":[0.9993002,0.0003009913,0.0001155461,0.00007205009,0.0001804976,0.00003069505],"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":[0.000508149,0.0003244449,0.004346439,0.000213645,0.000118352,0.0001794178,0.0001062036,0.4158638,0.0729825,0.001294769,0.001421997,0.5026403],"study_design_scores_gemma":[0.000005689401,0.00006786951,0.001113389,0.000005550075,0.00001092679,0.00002950028,0.00000830562,0.9879009,0.01038378,0.0003139781,0.0001554752,0.000004697077],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2373044,0.0006063217,0.7598132,0.0001800442,0.00002937963,0.00005718995,0.0001543001,0.0009188738,0.0009362448],"genre_scores_gemma":[0.8662943,0.0002946832,0.1312728,0.0000973567,0.00002318428,0.00004999626,0.0003348918,0.00005218066,0.001580439],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002993395,"threshold_uncertainty_score":0.005951941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01079277890249369,"score_gpt":0.2957333843654105,"score_spread":0.2849406054629168,"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."}}