{"id":"W2336915029","doi":"10.1177/0954410016640821","title":"Flight control clearance of the Cessna Citation X using evolutionary algorithms","year":2016,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering","topic":"Aerospace and Aviation Technology","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Differential evolution; Flight envelope; Fitness function; Genetic algorithm; Robustness (evolution); Flight dynamics; Control theory (sociology); Algorithm; Evolutionary algorithm; Flight simulator; Computer science; Weighting; Simulation; Engineering; Aerospace engineering; Artificial intelligence; Control (management); Aerodynamics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004514509,0.000537819,0.0003945401,0.0004396635,0.0003100785,0.0007643109,0.0003961046,0.0004865696,0.0009769989],"category_scores_gemma":[0.0007217667,0.000192564,0.0004456118,0.0002606838,0.0002463596,0.0002629729,0.0003405464,0.0003470358,0.0001213558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003735121,"about_ca_system_score_gemma":0.0005694773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00485072,"about_ca_topic_score_gemma":0.002627386,"domain_scores_codex":[0.9998428,0.00003503597,0.000009585649,0.00003962153,0.00005749591,0.00001555943],"domain_scores_gemma":[0.999821,0.00006563172,0.00003459099,0.00001607559,0.00005477176,0.000007878034],"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":[0.00009903064,0.0000512726,0.001379773,0.00009059422,0.00003825812,0.000101305,0.0000925364,0.8935479,0.01526479,0.003338772,0.0002615974,0.08573413],"study_design_scores_gemma":[0.0000118925,0.0001124791,0.0004960209,0.00000798491,0.00001146294,0.00001547723,0.00001607957,0.9958307,0.002306517,0.0003796989,0.0008056714,0.000006082361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2903532,0.0006424159,0.6955975,0.0001380332,0.00004909312,0.0001344933,0.00004132672,0.0005144613,0.01252956],"genre_scores_gemma":[0.9089066,0.0002044755,0.08724437,0.00003576676,0.00001040126,0.0001242542,0.00006113188,0.00002572407,0.00338736],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00485072,"threshold_uncertainty_score":0.009644985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007813359071484609,"score_gpt":0.189395227157802,"score_spread":0.1815818680863174,"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."}}