{"id":"W4306175617","doi":"10.1088/1755-1315/1079/1/012067","title":"Virtual sensors to generate turbine runner blade strains from indirect measurements","year":2022,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Turbomachinery Performance and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Strain gauge; Artificial neural network; Computer science; Term (time); Nonlinear system; Blade (archaeology); Engineering; Simulation; Real-time computing; Artificial intelligence; Structural 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001312453,0.000151958,0.0001152612,0.00008357922,0.0004527862,0.00009360283,0.0001796815,0.00002148991,0.0008664066],"category_scores_gemma":[0.000004641629,0.0001504383,0.00001593515,0.0002128268,0.000191741,0.0004941364,0.0001534334,0.0001317961,0.00002862583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006183532,"about_ca_system_score_gemma":0.0000238151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003359606,"about_ca_topic_score_gemma":0.00004316879,"domain_scores_codex":[0.9988425,0.00001794153,0.0001344329,0.0002817151,0.0004167045,0.0003066811],"domain_scores_gemma":[0.9996616,0.000003811566,0.000021313,0.0001307631,0.000004451483,0.0001780618],"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":[0.00001947151,0.00001835953,0.002522391,0.000002123215,0.000009719277,0.000004195512,0.001491419,0.3690644,0.611581,0.00001447408,0.00004770919,0.01522473],"study_design_scores_gemma":[0.0006566111,0.0005497562,0.2886996,0.00001412009,0.00002055962,0.0000283042,0.002868607,0.166538,0.5341215,0.00002391609,0.005653747,0.0008252316],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983736,0.00006046342,0.0001606026,0.00005573634,0.0002611859,0.00012518,0.0001474575,0.00006812043,0.0007477052],"genre_scores_gemma":[0.9987231,0.0001013451,0.000612399,0.000131374,0.00003910976,0.00001923177,0.00006431909,0.00001173267,0.0002973645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2861772,"threshold_uncertainty_score":0.9486545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01534742262074344,"score_gpt":0.1847558545925985,"score_spread":0.169408431971855,"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."}}