{"id":"W7147702000","doi":"10.5281/zenodo.19349341","title":"Aerodynamic Optimization Strategies in Gas Path Systems: A Turbine Design Exploration","year":2023,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Turbomachinery Performance and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Université du Québec à Montréal","funders":"","keywords":"Engineering design process; Propulsion; Parametrization (atmospheric modeling); Set (abstract data type); Aerodynamics; Multidisciplinary design optimization; Process (computing); Surrogate model; Path (computing); Optimization problem","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001538564,0.0004215444,0.0003707299,0.001227808,0.001551618,0.002464697,0.0008156204,0.0002429765,0.002193552],"category_scores_gemma":[0.0003064973,0.0004933003,0.00007050236,0.003319876,0.0001053429,0.002633271,0.0004825958,0.0005445459,0.005437345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005514139,"about_ca_system_score_gemma":0.000017259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004036463,"about_ca_topic_score_gemma":0.000001571933,"domain_scores_codex":[0.9964117,0.0007029999,0.0008152683,0.0006722718,0.0005882052,0.0008095655],"domain_scores_gemma":[0.9984511,0.0000507414,0.0002006371,0.0005889729,0.0005168762,0.0001916751],"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":[0.00009673256,0.00008734017,0.000002455577,0.000379433,0.00004458254,0.00003141309,0.002892297,0.969274,0.0008882013,0.00117983,0.01542888,0.009694786],"study_design_scores_gemma":[0.0009779143,0.0003000069,0.0002140594,0.0002806241,0.00002941341,0.00004313823,0.002447767,0.9724438,0.00004367687,0.0001023055,0.02264522,0.000472111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01843353,0.0006222095,0.952794,0.0004732521,0.0009648203,0.002384597,0.000268527,0.00439863,0.01966044],"genre_scores_gemma":[0.9784474,0.005427099,0.002601915,0.000024786,0.0003549685,0.000001035023,0.009445806,0.003175109,0.0005218823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9600139,"threshold_uncertainty_score":0.9997519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03763912255041846,"score_gpt":0.2316274064958888,"score_spread":0.1939882839454703,"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."}}