{"id":"W7117770396","doi":"10.53894/ijirss.v8i12.11110","title":"Data-driven torque identification of turboprop engines using optimized feedforward neural networks","year":2025,"lang":"","type":"article","venue":"International Journal of Innovative Research and Scientific Studies","topic":"Turbomachinery Performance and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Turboprop; Throttle; Propeller; Torque; Control theory (sociology); Artificial neural network; Mean squared error; Feedforward neural network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004557901,0.0002447404,0.0005631258,0.002326804,0.000434952,0.0005695643,0.001248831,0.0001043414,0.00001782119],"category_scores_gemma":[0.001618889,0.0002050926,0.00007455023,0.003024323,0.001242406,0.001506103,0.0008862143,0.0007402098,8.981625e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002952105,"about_ca_system_score_gemma":0.0002860639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001290716,"about_ca_topic_score_gemma":0.000008612869,"domain_scores_codex":[0.9960352,0.0001770556,0.00170258,0.0004014983,0.001291261,0.0003923664],"domain_scores_gemma":[0.983824,0.0004592467,0.0006795331,0.0003767746,0.01459183,0.00006863532],"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":[0.0005919356,0.0001911637,0.005663498,0.0002831328,0.003069636,0.00002746148,0.001702675,0.9511744,0.01336122,0.001556163,0.00472451,0.01765418],"study_design_scores_gemma":[0.001061137,0.0001117406,0.003490133,0.0007300759,0.00003930923,0.00002330458,0.001998858,0.9886174,0.003018783,0.000358415,0.0003966768,0.0001541311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8638493,0.02754918,0.09507575,0.001092758,0.01157012,0.0004398626,0.0002562589,0.00001717927,0.0001495817],"genre_scores_gemma":[0.9913123,0.004351311,0.003241269,0.00001155995,0.000477352,0.000004288097,0.00006375207,0.00001648506,0.0005217401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1274629,"threshold_uncertainty_score":0.8363433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1293620206235732,"score_gpt":0.4310178483519892,"score_spread":0.3016558277284159,"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."}}