{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002196755,0.0001118447,0.00009566769,0.00003980936,0.0002587687,0.00008788095,0.0001039483,0.00006788052,0.0001053158],"category_scores_gemma":[0.00002787027,0.00007769138,0.0000342934,0.0000605775,0.00008150573,0.0001701203,0.00002589605,0.0001482542,0.00002081786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005056903,"about_ca_system_score_gemma":0.000008673609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.260624e-7,"about_ca_topic_score_gemma":0.000001022652,"domain_scores_codex":[0.9992373,0.00002695748,0.0002133233,0.000102827,0.0001884763,0.0002311409],"domain_scores_gemma":[0.99968,0.00005563356,0.00001747669,0.00009657191,0.00004543987,0.0001048937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00220963,0.0007332222,0.005046503,0.0005999025,0.002084035,0.00005374581,0.01230539,0.2654857,0.007622585,0.04978118,0.3827952,0.2712829],"study_design_scores_gemma":[0.0008933087,0.0002491474,0.004449309,0.00003680901,0.0001084462,0.00004822373,0.00008354449,0.9851822,0.0005520737,0.001032815,0.007236738,0.0001274385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2431868,0.0002410641,0.7485651,0.002637503,0.001377326,0.0005652524,0.00001587701,0.0003022321,0.003108763],"genre_scores_gemma":[0.9922182,0.0001105406,0.006749722,0.0001445159,0.0006185747,0.00001076788,0.00002113178,0.00002229479,0.0001042395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7490314,"threshold_uncertainty_score":0.3168163,"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."}}