{"id":"W2986657226","doi":"10.1115/gt2019-90610","title":"Optimized SGT-A35 (GT61) for Improved Emissions and Enhanced Efficiency Across the Load Range","year":2019,"lang":"en","type":"article","venue":"Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy","topic":"Turbomachinery Performance and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada)","funders":"","keywords":"Turbine; Greenhouse gas; Automotive engineering; Process engineering; Environmental science; Reliability engineering; Computer science; Manufacturing engineering; Engineering; Mechanical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003027834,0.0003232497,0.0003214929,0.0004158853,0.0001807318,0.0005068581,0.0004540492,0.0004444291,0.003773809],"category_scores_gemma":[0.0003184203,0.0001275173,0.000349933,0.0005931485,0.0001909324,0.0004617396,0.000222032,0.0004080705,0.001220592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003419004,"about_ca_system_score_gemma":0.0003578666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001609309,"about_ca_topic_score_gemma":0.003245056,"domain_scores_codex":[0.9996713,0.00002700429,0.00001944961,0.00006241742,0.0001759555,0.00004386399],"domain_scores_gemma":[0.999818,0.00001350716,0.00002685366,0.0000216696,0.00009465055,0.00002540537],"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":[0.0005113566,0.0002871366,0.001591317,0.0002550912,0.00002285621,0.0002966032,0.00007857374,0.01410732,0.9318272,0.001469551,0.004024513,0.04552843],"study_design_scores_gemma":[0.0002686964,0.00533385,0.02356705,0.00002682638,0.00006815312,0.0005982062,0.0001077151,0.04947556,0.8605329,0.000472193,0.05946188,0.00008705527],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9571597,0.0004127496,0.02187718,0.0001979971,0.0001342156,0.0002383364,0.001084245,0.001296017,0.01759958],"genre_scores_gemma":[0.9771713,0.0001002269,0.01578249,0.00005640281,0.000008681724,0.00003544182,0.0008059283,0.0001734751,0.005866027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003773809,"threshold_uncertainty_score":0.01262462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003676020356944372,"score_gpt":0.2152619790924899,"score_spread":0.2115859587355455,"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."}}