{"id":"W4413219341","doi":"10.1115/gt2025-153546","title":"Surrogate Modelling of a Turboprop Engine Performance","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Aircraft Design and Technologies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Turboprop; Perceptron; Artificial neural network; Computer science; Fuel efficiency; Thrust specific fuel consumption; Mean absolute percentage error; Latin hypercube sampling; Design of experiments; Surrogate model; Performance prediction; Simulation; Machine learning; Engineering; Monte Carlo method; Automotive engineering; Statistics; Mathematics","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":[0.00005285793,0.00005422446,0.00007287582,0.00002772666,0.00002836018,0.000002329385,0.0001325273,0.0000307013,0.000227938],"category_scores_gemma":[0.000007650401,0.00004235772,0.00001837539,0.0002214787,0.00009307752,0.00009327477,0.00008980413,0.00005343261,0.00004453171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002336886,"about_ca_system_score_gemma":0.000002915566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002526117,"about_ca_topic_score_gemma":0.000002199922,"domain_scores_codex":[0.9996114,0.000003137216,0.00009529066,0.0001118653,0.00006920216,0.0001090665],"domain_scores_gemma":[0.9998064,0.00001511113,0.00001863584,0.000146379,0.000003344832,0.00001014903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002767253,0.00008034378,0.03294514,0.00004824531,0.00001451976,0.0000023397,0.00007930539,0.5639078,0.02240927,0.004108306,0.0005252654,0.3758518],"study_design_scores_gemma":[0.000257796,0.00005369381,0.003044243,0.00003174115,0.000006827218,0.00000100303,0.00006694611,0.4833842,0.4996461,0.009861139,0.003505442,0.0001409039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6596125,0.0000382158,0.2977447,0.0001083072,0.00002818133,0.0000688151,3.83093e-7,0.000123966,0.04227493],"genre_scores_gemma":[0.9660892,0.00006035346,0.03001995,0.00003692454,0.000001318352,0.000004511377,3.117708e-7,0.000002419896,0.003784952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4772368,"threshold_uncertainty_score":0.2495761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01396277438110093,"score_gpt":0.2059643622160953,"score_spread":0.1920015878349944,"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."}}