{"id":"W4410411432","doi":"10.2514/1.i011399","title":"Predicting Lateral Dynamics of CRJ700 Using Multilayer Perceptron and Support Vector Regression","year":2025,"lang":"en","type":"article","venue":"Journal of Aerospace Information Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Canada Research Chairs","keywords":"Support vector machine; Regression; Regression analysis; Dynamics (music); Computer science; Artificial intelligence; Statistics; Mathematics; Psychology","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":[0.0002969548,0.00008002442,0.00019189,0.0001407696,0.00009612508,0.0001770687,0.0002170379,0.0000560743,0.000001393609],"category_scores_gemma":[0.00001948581,0.00005975716,0.00004901424,0.0002291127,0.00002109665,0.001738993,0.00008053162,0.0001334129,0.000001088461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007463749,"about_ca_system_score_gemma":0.00006793663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002668675,"about_ca_topic_score_gemma":0.000002541583,"domain_scores_codex":[0.9989477,0.0000301347,0.0006321998,0.00005482058,0.0002288023,0.0001063109],"domain_scores_gemma":[0.9986574,0.0000459063,0.0008025939,0.0001488374,0.0002943948,0.00005084204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003371011,0.0003200767,0.4733932,0.0027749,0.0004461515,0.00002463245,0.0328545,0.2665002,0.03475555,0.1069522,0.01664629,0.0649952],"study_design_scores_gemma":[0.0004117147,0.00006637091,0.01015475,0.0004558269,0.00001247767,0.0001357936,0.0005352708,0.9867393,0.0003581569,0.00002303317,0.001034647,0.00007268738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7202831,0.00007771723,0.2777924,0.0006291227,0.0006996995,0.0001694409,0.000004616179,0.00001823986,0.0003256428],"genre_scores_gemma":[0.9948347,0.00002166843,0.004964084,0.00004377473,0.00004110214,0.000001479673,0.000001519721,0.000002237158,0.0000894131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.720239,"threshold_uncertainty_score":0.2436826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01220437619365173,"score_gpt":0.2704177865399051,"score_spread":0.2582134103462534,"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."}}