{"id":"W4382243047","doi":"10.1007/978-3-031-29933-9_30","title":"Aerodynamic Shape Optimization of the Morphing Leading Edge for the UAS-S45 Winglet","year":2023,"lang":"en","type":"book-chapter","venue":"Sustainable aviation","topic":"Aeroelasticity and Vibration Control","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Wingtip device; Airfoil; Aerodynamics; Leading edge; Lift-induced drag; Aerospace engineering; Morphing; Engineering; Lift-to-drag ratio; Drag; Structural engineering; Computer science; Artificial intelligence","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":[0.0003248773,0.0002235435,0.0002373244,0.0001237164,0.0003370679,0.00007797341,0.0002513974,0.000234238,0.00006908793],"category_scores_gemma":[0.0002584729,0.0001720549,0.0001597829,0.0001180453,0.00004278364,0.0002413963,0.00005835688,0.0002591927,0.00001334849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002713517,"about_ca_system_score_gemma":0.00007643649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001058065,"about_ca_topic_score_gemma":0.00002388713,"domain_scores_codex":[0.9989375,0.00001531017,0.0003701001,0.000182395,0.0002279105,0.0002667339],"domain_scores_gemma":[0.9987756,0.0004164376,0.0002140942,0.0002902268,0.0002802944,0.00002335293],"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":[0.00001014789,0.000002415259,0.000005466876,0.0003572421,0.00007643366,6.895932e-7,0.0002221553,0.855811,0.00006874641,0.1414533,0.0009663982,0.001026054],"study_design_scores_gemma":[0.0002759926,0.0000256986,0.00005810564,0.0001280447,0.0001675371,8.095965e-7,0.0002763445,0.9866416,0.00005365719,0.006290045,0.005887838,0.0001943674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001854643,0.0003573564,0.9725811,0.0007484467,0.001120885,0.002161372,0.00005940828,0.0003839915,0.02240191],"genre_scores_gemma":[0.5957476,0.0002112614,0.0006103042,0.0001260755,0.0008332413,0.0002394663,0.0002781907,0.0002919691,0.4016618],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9719709,"threshold_uncertainty_score":0.7016194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01181050792776941,"score_gpt":0.2015419730883647,"score_spread":0.1897314651605953,"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."}}