{"id":"W3115362096","doi":"10.1088/2631-8695/abd5a6","title":"Numerical assessment of blade deflection and elongation for improved monitoring of blade and TBC damage","year":2020,"lang":"en","type":"article","venue":"Engineering Research Express","topic":"Fatigue and fracture mechanics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Deflection (physics); Spallation; Turbine blade; Structural engineering; Creep; Deflexion; Materials science; Forensic engineering; Engineering; Mechanical engineering; Composite material; Finite element method; Turbine; Optics; Physics","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":[0.0002463193,0.0001011695,0.0001776308,0.000103194,0.00003300019,0.00002250431,0.00008141988,0.00007454829,0.000002019446],"category_scores_gemma":[0.0001813314,0.000106628,0.00002529129,0.0001840846,0.00001860207,0.0001222478,0.00003854416,0.0002701903,1.061459e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003012927,"about_ca_system_score_gemma":0.00001251419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001482026,"about_ca_topic_score_gemma":2.580327e-7,"domain_scores_codex":[0.9992577,0.00001842686,0.0001847567,0.0001491641,0.0001760227,0.0002138949],"domain_scores_gemma":[0.9994619,0.0002265871,0.0000223777,0.000106501,0.00007866514,0.0001039902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001460179,0.00001019239,0.000462612,0.0008398284,0.00003731663,6.135498e-7,0.0005260343,0.0644114,0.9313046,0.0002143601,0.00003861777,0.002139778],"study_design_scores_gemma":[0.0002513022,0.0001297287,0.003199441,0.00006060794,0.000006316518,5.000523e-7,0.0000666716,0.6887392,0.3072509,0.00003599509,0.0001834759,0.00007579564],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5442544,0.0003985263,0.4546643,0.00008218836,0.0001251562,0.0003418704,0.00001133677,0.00008412463,0.00003810426],"genre_scores_gemma":[0.9783804,0.0001296208,0.02128744,0.000001433442,0.000117547,0.00004508131,0.00000446502,0.00003101287,0.000003040732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6243278,"threshold_uncertainty_score":0.4348165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04678130081999553,"score_gpt":0.3344981863360934,"score_spread":0.2877168855160979,"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."}}