{"id":"W2942219242","doi":"10.1504/ijscom.2018.10020805","title":"Numerical and experimental comparisons of pressed blades for large Francis turbine runners manufactured with a reconfigurable pressing setup and a conventional setup","year":2018,"lang":"en","type":"article","venue":"International Journal of Service and Computing Oriented Manufacturing","topic":"Metallurgy and Material Forming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Pressing; Finite element method; Hydraulic press; Mechanical engineering; Francis turbine; Turbine blade; Die (integrated circuit); Range (aeronautics); Process (computing); Hydropower; Engineering; Turbine; Drum; Power (physics); Structural engineering; Computer science; Electrical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000165758,0.0001552847,0.0002655051,0.0001164967,0.0001086974,0.00007535914,0.0001121322,0.00005299317,0.00003437582],"category_scores_gemma":[0.000008105602,0.0001311224,0.00003474273,0.00003146004,0.00004519307,0.0002594493,0.00004435264,0.0001184835,2.758378e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000199342,"about_ca_system_score_gemma":0.00001231607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000029281,"about_ca_topic_score_gemma":0.00000584006,"domain_scores_codex":[0.9991009,0.00001543083,0.0003754564,0.0001383046,0.0001981503,0.0001717532],"domain_scores_gemma":[0.9993986,0.0000770396,0.0002221441,0.00005287389,0.0001658394,0.0000834935],"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":[0.01455117,0.001425561,0.01012554,0.005983799,0.01054318,0.0002098157,0.04436383,0.05347332,0.8213417,0.005371917,0.001790845,0.03081933],"study_design_scores_gemma":[0.004310439,0.0003749624,0.007020708,0.0008262807,0.00009138395,0.0004483831,0.001308907,0.07476801,0.9064413,0.0001681563,0.0039469,0.0002946091],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9740936,0.0003537549,0.02469701,0.0001024937,0.0005169718,0.000108467,0.00001879257,0.00002383456,0.0000851002],"genre_scores_gemma":[0.9945619,0.00001366903,0.005074548,0.0000869069,0.0002208613,0.000002034629,0.00001350774,0.00001739618,0.00000914529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08509957,"threshold_uncertainty_score":0.5347016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008937208106471086,"score_gpt":0.244496308881717,"score_spread":0.2355591007752459,"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."}}