{"id":"W4235191408","doi":"10.1115/1.1645533","title":"The Influence of Leading-Edge Geometry on Secondary Losses in a Turbine Cascade at the Design Incidence","year":2004,"lang":"en","type":"article","venue":"Journal of Turbomachinery","topic":"Turbomachinery Performance and Optimization","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Airfoil; Cascade; Trailing edge; Leading edge; Secondary flow; Vortex; Inlet; Mechanics; Flow (mathematics); Turbine; Enhanced Data Rates for GSM Evolution; Upstream (networking); Geometry; Physics; Materials science; Structural engineering; Engineering; Mechanical engineering; Mathematics; Turbulence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000379157,0.0005471561,0.0005891285,0.0003622211,0.000582418,0.0004878615,0.0002853391,0.000313603,0.000991188],"category_scores_gemma":[0.001229469,0.0003175881,0.0002390693,0.0001528906,0.0007043244,0.0005841127,0.0004433678,0.0004797994,0.0002488158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004903224,"about_ca_system_score_gemma":0.0002388562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001252771,"about_ca_topic_score_gemma":0.001553348,"domain_scores_codex":[0.9996251,0.00004247324,0.00001251479,0.00007462039,0.000148909,0.00009638746],"domain_scores_gemma":[0.9991637,0.0003662302,0.0001438995,0.0000528834,0.0001861787,0.0000871599],"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":[0.001217825,0.0001631598,0.01174582,0.00005796962,0.00001038558,0.0004930623,0.0002230498,0.006809908,0.9713495,0.00019469,0.00009303163,0.007641653],"study_design_scores_gemma":[0.0001011328,0.002882474,0.06318411,0.00001376059,0.00004559399,0.0003799494,0.0002120205,0.02985804,0.9026881,0.00017486,0.0004237126,0.00003621091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982645,0.00002340245,0.001265948,0.000007783982,0.000002456593,0.000008878912,0.00002034472,0.00002298396,0.0003836436],"genre_scores_gemma":[0.9991967,0.00001702024,0.0005639211,0.000004414303,0.000001614071,0.000004627165,0.00002015757,0.00000797947,0.0001834651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001252771,"threshold_uncertainty_score":0.003557563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007698989033305376,"score_gpt":0.2260684812347699,"score_spread":0.2183694922014645,"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."}}