{"id":"W3022678419","doi":"10.2514/1.i010791","title":"New Methodology for Aircraft Performance Model Identification for Flight Management System Applications","year":2020,"lang":"en","type":"article","venue":"Journal of Aerospace Information Systems","topic":"Aerospace and Aviation Technology","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Canada Research Chairs","keywords":"Aerodynamics; Avionics; Fly-by-wire; Trajectory; Flight simulator; Aircraft flight mechanics; Flight management system; Aerospace engineering; Fuel efficiency; Thrust; Drag; Simulation; Computer science; Experimental data; Flight dynamics; 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":[0.0005439814,0.0001468957,0.0003329718,0.0001863163,0.000101299,0.0000954007,0.0002806302,0.0001466717,0.000001115273],"category_scores_gemma":[0.00003031216,0.0001405152,0.0001243304,0.0002468647,0.00001165249,0.001096143,0.00001708423,0.0001193852,0.00003511366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000157607,"about_ca_system_score_gemma":0.00004301281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.922609e-7,"about_ca_topic_score_gemma":3.21423e-7,"domain_scores_codex":[0.9984967,0.00001524721,0.001006224,0.00008242472,0.0002084032,0.0001910349],"domain_scores_gemma":[0.9985877,0.00006727387,0.0006689082,0.0001734346,0.0003839408,0.0001187491],"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":[0.00007960351,0.000007378145,0.00006808196,0.003529079,0.0001928432,1.731576e-7,0.001437064,0.8808313,0.001785965,0.04992114,0.0480111,0.0141363],"study_design_scores_gemma":[0.001236439,0.0001307967,0.00004368933,0.00008797694,0.0001012679,0.00002724541,0.002839643,0.8543156,0.006782006,0.00006880168,0.1341677,0.0001987886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001259139,0.0002328386,0.9952298,0.0009022211,0.0005050195,0.001332475,0.00003181951,0.0002248134,0.0002818368],"genre_scores_gemma":[0.8140513,0.000163584,0.1842716,0.000128027,0.0004343257,0.0005241119,0.00004387752,0.0000347618,0.0003483259],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8127922,"threshold_uncertainty_score":0.5730045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0315694253409682,"score_gpt":0.249932706343137,"score_spread":0.2183632810021688,"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."}}