{"id":"W2872292047","doi":"10.1017/aer.2018.67","title":"Vertical flight path segments sets for aircraft flight plan prediction and optimisation","year":2018,"lang":"en","type":"article","venue":"The Aeronautical Journal","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Climb; Descent (aeronautics); Cruise; Flight envelope; Flight test; Flight plan; Envelope (radar); Aerospace engineering; Computation; Computer science; Flight management system; Path (computing); Range (aeronautics); Flight simulator; Simulation; Algorithm; Radar; Engineering; Aerodynamics","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":[0.000485653,0.001036276,0.0006645838,0.001744768,0.0004063753,0.0005500771,0.0007478676,0.0005400214,0.00354869],"category_scores_gemma":[0.00230595,0.0005144052,0.001010453,0.001124283,0.000394894,0.000615537,0.0007889565,0.0008236761,0.0004411787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008041502,"about_ca_system_score_gemma":0.000874659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01225031,"about_ca_topic_score_gemma":0.006497275,"domain_scores_codex":[0.9995467,0.0001131202,0.00002287552,0.00008311415,0.0001890673,0.00004511388],"domain_scores_gemma":[0.9990689,0.0005369745,0.0001052875,0.00009111488,0.0001730492,0.00002465639],"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":[0.00008998869,0.000051527,0.001157301,0.00008366218,0.0000312564,0.00004243672,0.00006410731,0.8971168,0.002755507,0.002523922,0.000728448,0.09535506],"study_design_scores_gemma":[0.000004401447,0.00002662939,0.0002999526,0.00001031528,0.000004205975,0.000006336471,0.0000110252,0.9971603,0.0007703289,0.001231334,0.0004718474,0.000003398481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06102151,0.0002081071,0.9338398,0.00006916653,0.0000198928,0.0001907054,0.001065005,0.001582765,0.002002963],"genre_scores_gemma":[0.4462577,0.0001362182,0.5481395,0.0000287621,0.00002080344,0.0003833692,0.00359535,0.0002410153,0.001197347],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01225031,"threshold_uncertainty_score":0.02435797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01446248127055898,"score_gpt":0.2257516030959756,"score_spread":0.2112891218254166,"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."}}