{"id":"W2505552806","doi":"10.1016/j.ndteint.2016.07.005","title":"Non-destructive and non-contacting stress–strain characterization of aerospace metallic alloys using photo-thermo-mechanical radiometry","year":2016,"lang":"en","type":"article","venue":"NDT & E International","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Aerospace; Materials science; Characterization (materials science); Radiometry; Nondestructive testing; Aerospace materials; Metal; Stress (linguistics); Strain (injury); Composite material; Metallurgy; Forensic engineering; Optics; Nanotechnology; Engineering; Aerospace engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001255345,0.0001544472,0.0001859342,0.00019976,0.00002901157,0.00002566632,0.0001609382,0.0001102421,0.0001878765],"category_scores_gemma":[0.0000235262,0.0001325082,0.00007055093,0.000134093,0.00005044173,0.0002567955,0.00003375546,0.0001086828,7.143585e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006909578,"about_ca_system_score_gemma":0.0000131693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002674458,"about_ca_topic_score_gemma":0.000003773534,"domain_scores_codex":[0.9992153,0.00001423685,0.0002463549,0.0001806475,0.000190832,0.0001526002],"domain_scores_gemma":[0.9995793,0.00007196808,0.0001048153,0.0001187861,0.00007150984,0.00005365352],"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":[0.00002343546,0.00001987397,0.002696218,0.00002176898,0.0001414649,0.000003141642,0.0001565922,0.00001382847,0.9917202,0.0002759433,0.000002447045,0.004925085],"study_design_scores_gemma":[0.0005608425,0.00005136024,0.04787495,0.0002870032,0.00004022989,0.00002833407,0.00006022002,0.008663832,0.9415943,0.0005183999,0.0000774012,0.0002431343],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9122748,0.00001176237,0.08564946,0.00002089074,0.0003275954,0.0001474216,0.00124457,0.0000879181,0.0002355429],"genre_scores_gemma":[0.9976146,0.00004531676,0.00208902,0.00001452748,0.0001262967,0.00001473419,0.00003977714,0.00003182488,0.00002396161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0853397,"threshold_uncertainty_score":0.5403528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0089257207138922,"score_gpt":0.2269627750775816,"score_spread":0.2180370543636894,"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."}}