{"id":"W4308124840","doi":"10.3390/designs6060102","title":"Optimization and Design of a Flexible Droop Nose Leading Edge Morphing Wing Based on a Novel Black Widow Optimization (B.W.O.) Algorithm—Part II","year":2022,"lang":"en","type":"article","venue":"Designs","topic":"Aeroelasticity and Vibration Control","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Canada Research Chairs","keywords":"Airfoil; Stall (fluid mechanics); Leading edge; Wing; Morphing; Drag; Lift coefficient; Trailing edge; Fitness function; Angle of attack; Engineering; Mathematics; Aerodynamics; Control theory (sociology); Algorithm; Genetic algorithm; Structural engineering; Computer science; Mathematical optimization; Aerospace engineering; Mechanics; Reynolds number; Physics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003408047,0.0001801765,0.0002240271,0.0002235733,0.0003503292,0.00004912122,0.0001110814,0.00006190855,0.0002503943],"category_scores_gemma":[0.00006558868,0.0002182697,0.00004207104,0.0003816107,0.00003562518,0.0002738005,0.00004089236,0.0001645004,0.000002255113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001042041,"about_ca_system_score_gemma":0.00004639636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004142773,"about_ca_topic_score_gemma":2.664659e-7,"domain_scores_codex":[0.9989097,0.00008089116,0.0002995892,0.0002296586,0.000248938,0.0002312519],"domain_scores_gemma":[0.9993988,0.0002293364,0.00008915569,0.0001606343,0.00005136663,0.00007072565],"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":[0.00004388814,0.0000781861,0.000009779646,0.00002662746,0.00002842936,0.000002453403,0.0005678521,0.9904124,0.007560096,0.0002047985,0.0003890741,0.0006764074],"study_design_scores_gemma":[0.001018704,0.0002970433,0.000006521445,0.00004584346,0.00004134517,0.000003257235,0.00011768,0.9942504,0.003908282,0.000009198109,0.00009190579,0.0002098088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006709932,0.00004313528,0.9979834,0.00006326594,0.0002296468,0.0004470526,0.00003030639,0.0002175781,0.0003146306],"genre_scores_gemma":[0.654891,0.000008821217,0.3443492,0.0001947877,0.0001092249,0.0001223542,0.00006776062,0.00006906595,0.0001877757],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.65422,"threshold_uncertainty_score":0.8900779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03743736356590247,"score_gpt":0.2259073551347978,"score_spread":0.1884699915688954,"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."}}