{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005437331,0.0004345617,0.0003984447,0.0003872411,0.0001568465,0.0006301678,0.0003941354,0.0006307885,0.001309243],"category_scores_gemma":[0.001679725,0.0001770332,0.0003781767,0.0003504129,0.0002817454,0.000444723,0.0003308267,0.0004513491,0.0002488538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000315246,"about_ca_system_score_gemma":0.0003656878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001485313,"about_ca_topic_score_gemma":0.000789913,"domain_scores_codex":[0.9997612,0.00008511378,0.00001291876,0.0000323605,0.00009132717,0.00001725357],"domain_scores_gemma":[0.9994951,0.0002719013,0.00005851659,0.00005595724,0.0001044292,0.00001410551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005063245,0.0000215824,0.0006258875,0.0000192078,0.00001032485,0.00002798903,0.000006595544,0.9940639,0.002139973,0.0004215729,0.00007436047,0.002537978],"study_design_scores_gemma":[0.000001530379,0.00002481504,0.0002284797,0.000002126901,0.000001300947,0.000004979654,0.000001451623,0.9981837,0.001331201,0.0001464523,0.00007223721,0.000001790309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7207152,0.0002665517,0.2676232,0.0001589629,0.00005930488,0.000068711,0.0008388722,0.0004759488,0.009793213],"genre_scores_gemma":[0.9936042,0.00005110379,0.005148073,0.000009384033,0.000002610356,0.00003813277,0.0002322988,0.0000106427,0.0009036766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001485313,"threshold_uncertainty_score":0.004379869,"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."}}