{"id":"W2909576594","doi":"10.2514/6.2019-0432.c1","title":"Correction: New Algorithm for Fuel Flow Prediction and its Correction during the Cruise - Application on the Cessna Citation X Business Aircraft","year":2019,"lang":"en","type":"article","venue":"AIAA Scitech 2019 Forum","topic":"Aerospace and Aviation Technology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec","funders":"","keywords":"Cruise; Flow (mathematics); Computer science; Citation; Algorithm; Aeronautics; Operations research; Engineering; Aerospace engineering; Mechanics; Library science; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001672194,0.004456001,0.002704171,0.005359825,0.003176439,0.004773362,0.004027513,0.006825691,0.1328824],"category_scores_gemma":[0.05516677,0.001469002,0.001995084,0.004087897,0.001843544,0.003446515,0.00311542,0.007124241,0.07387534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003051562,"about_ca_system_score_gemma":0.00422278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02941076,"about_ca_topic_score_gemma":0.02472859,"domain_scores_codex":[0.9958708,0.0004453586,0.0006384981,0.0007898292,0.00180444,0.000451023],"domain_scores_gemma":[0.9638981,0.005336193,0.001288855,0.003470216,0.02463084,0.00137569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001049758,0.00001275612,0.0001782027,0.0001667711,0.00002608264,0.0001448467,0.00002943101,0.00048291,0.000297828,0.0007039093,0.9839498,0.01390256],"study_design_scores_gemma":[0.0001971883,0.00006462599,0.002560525,0.0002669556,0.00005843413,0.0006280476,0.0001405068,0.01134493,0.003824515,0.003077136,0.9776959,0.0001412825],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.000966535,0.0006046685,0.0103851,0.01625093,0.9556834,0.0000604975,0.005098175,0.008380325,0.002570445],"genre_scores_gemma":[0.09323913,0.004478765,0.1182258,0.05306999,0.2908019,0.000697528,0.02764418,0.02502349,0.3868192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1328824,"threshold_uncertainty_score":0.4445361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004718569236529779,"score_gpt":0.1918468522473313,"score_spread":0.1871282830108015,"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."}}