{"id":"W4400614524","doi":"10.1016/j.compscitech.2024.110759","title":"Explainable artificial intelligence prediction of defect characterization in composite materials","year":2024,"lang":"en","type":"article","venue":"Composites Science and Technology","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Texas A and M University","keywords":"Thermography; Finite element method; Materials science; Transfer of learning; Benchmark (surveying); Heat transfer; Characterization (materials science); Computer science; Inverse problem; Void (composites); Inverse; Nondestructive testing; Artificial intelligence; Machine learning; Infrared; Structural engineering; Composite material; Optics; Mechanics; Mathematics; 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":[0.0005159808,0.0002935298,0.0002379059,0.0005859673,0.0001796812,0.0005699361,0.000382095,0.0005177295,0.0009518797],"category_scores_gemma":[0.00305495,0.0001605431,0.0003572623,0.0001968381,0.0004006094,0.0006017751,0.0002277321,0.0005224805,0.00007727422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000623722,"about_ca_system_score_gemma":0.0002678424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003490665,"about_ca_topic_score_gemma":0.003764729,"domain_scores_codex":[0.9998825,0.00004037255,0.000005986587,0.00003744238,0.00001679874,0.00001697518],"domain_scores_gemma":[0.9978111,0.001669596,0.0002263188,0.0001264297,0.0001330318,0.00003352668],"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":[0.0001634597,0.00009500405,0.0268148,0.00006888482,0.0000927739,0.0001568771,0.00008506416,0.9334297,0.00681286,0.01196736,0.0007320084,0.01958125],"study_design_scores_gemma":[0.000001580349,0.000005366173,0.001539307,0.000001263183,0.000003194235,0.000006417871,0.000004294757,0.9947672,0.0003992975,0.003237513,0.00003300517,0.000001555461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8867061,0.0002632095,0.1097985,0.0004740255,0.0000361709,0.00002126848,0.0003183958,0.0003085651,0.002073906],"genre_scores_gemma":[0.9961897,0.00002876006,0.003492358,0.00001196104,0.000005915512,0.000004452387,0.0000701223,0.000006798441,0.0001898767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003490665,"threshold_uncertainty_score":0.006940663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00991524094260036,"score_gpt":0.2169606090643569,"score_spread":0.2070453681217565,"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."}}