{"id":"W4390947360","doi":"10.2514/1.i011332","title":"Neural Networks and Support Vector Regression for the CRJ-700 Longitudinal Dynamics Modeling","year":2024,"lang":"en","type":"article","venue":"Journal of Aerospace Information Systems","topic":"Aerospace and Aviation Technology","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Aerodynamics; Support vector machine; Flight dynamics; Computer science; Hyperparameter; Engineering; Simulation; Artificial intelligence; Machine learning; Aerospace engineering","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":[0.0004267202,0.0001255653,0.0001959123,0.0001291659,0.0001029924,0.0003087202,0.0001228379,0.0001301236,0.00000281649],"category_scores_gemma":[0.00002713586,0.00008004063,0.00008200781,0.000148709,0.00002198696,0.001210001,0.00001966698,0.0003028391,0.000003931928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001093019,"about_ca_system_score_gemma":0.00002205216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004431886,"about_ca_topic_score_gemma":0.000004222765,"domain_scores_codex":[0.9990597,0.000009545198,0.0005279933,0.00004794748,0.0001889373,0.0001658174],"domain_scores_gemma":[0.9994219,0.00009654207,0.0001684943,0.0001066319,0.0001561612,0.00005029637],"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":[0.00001507987,0.000001689755,0.0004276131,0.0001944973,0.00005908784,0.000002124451,0.0004059281,0.9898592,0.00001841076,0.001534082,0.003975987,0.003506343],"study_design_scores_gemma":[0.0001960221,0.00007432162,0.000138983,0.0001548048,0.0000337743,0.0002221541,0.0009094593,0.9950122,0.00002034057,0.00001261117,0.003138319,0.00008706129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08768546,0.002715498,0.904977,0.001076257,0.002981925,0.0002772766,0.00001378603,0.0001689586,0.0001038039],"genre_scores_gemma":[0.9990395,0.0003969089,0.0001447091,0.00002171946,0.000280598,0.00001243871,0.000008503071,0.0000153866,0.00008021759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9113541,"threshold_uncertainty_score":0.3263962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117367997856423,"score_gpt":0.2341677514329155,"score_spread":0.2224309516472733,"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."}}