{"id":"W3167308378","doi":"10.3390/s21124026","title":"Aircraft Fuselage Corrosion Detection Using Artificial Intelligence","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Dalhousie University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo; Nvidia","keywords":"Fuselage; Corrosion; Aerospace; Automation; Identification (biology); Engineering; Economic shortage; Airworthiness; Artificial intelligence; Aircraft maintenance; Task (project management); Computer science; Systems engineering; Structural engineering; Aeronautics; Aerospace engineering; Mechanical engineering; Materials science","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":[0.00008703654,0.0001197033,0.0001088639,0.00007161959,0.00007195116,0.00003242627,0.00006041717,0.00008365871,0.00003754221],"category_scores_gemma":[0.0001312788,0.0001413789,0.00004202146,0.0003011618,0.00003460977,0.00006917809,0.00003011803,0.0001814617,0.00003641962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001178151,"about_ca_system_score_gemma":0.00001421475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001845653,"about_ca_topic_score_gemma":0.00003064122,"domain_scores_codex":[0.9993156,0.00003472857,0.0001757349,0.0001737948,0.0001124235,0.0001877556],"domain_scores_gemma":[0.9996068,0.00005570734,0.00002307677,0.0002010894,0.00006974508,0.00004356266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003144538,0.00000954546,0.000127812,0.00002238257,0.000004029195,0.00004709184,0.0001310622,0.007501059,0.9801729,0.001780935,0.000004473791,0.01019551],"study_design_scores_gemma":[0.00001265581,0.00001510187,0.0002838015,0.00004205833,0.00001002062,0.00009053441,0.0001251337,0.07395644,0.872902,0.05237588,0.00002288546,0.0001634553],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.792169,0.00003082336,0.2051814,0.000006522794,0.0003881648,0.000058565,0.000002129912,0.0009824359,0.001180973],"genre_scores_gemma":[0.8302605,0.000008383795,0.1695973,0.000007376609,0.0000850606,0.000002011732,0.000001788346,0.00003279875,0.000004768455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1072709,"threshold_uncertainty_score":0.5765263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03583568003301097,"score_gpt":0.2642771769685478,"score_spread":0.2284414969355368,"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."}}