{"id":"W1930479197","doi":"10.2514/1.i010348","title":"Flight Trajectory Optimization Through Genetic Algorithms for Lateral and Vertical Integrated Navigation","year":2015,"lang":"en","type":"article","venue":"Journal of Aerospace Information Systems","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Federal Aviation Administration; Consejo Nacional de Ciencia y Tecnología","keywords":"Cruise; Trajectory; Trajectory optimization; Genetic algorithm; Fuel efficiency; Simulation; Reduction (mathematics); Aerospace engineering; Computer science; Control theory (sociology); Algorithm; Engineering; Mathematical optimization; Mathematics; 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":[0.0006409098,0.000880806,0.0007613204,0.0008974514,0.0004727278,0.0007092032,0.0007740396,0.001016794,0.001779905],"category_scores_gemma":[0.001720594,0.0004307072,0.0006747941,0.0009855166,0.0005696201,0.0003924722,0.0006763293,0.0007774063,0.0002976691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001105184,"about_ca_system_score_gemma":0.001691858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02060697,"about_ca_topic_score_gemma":0.01319673,"domain_scores_codex":[0.9997436,0.0000854847,0.00001076008,0.00004013502,0.00007482735,0.00004516565],"domain_scores_gemma":[0.9994825,0.0003055637,0.00005931368,0.00002262477,0.0001086182,0.00002129561],"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":[0.00001145132,0.00001659353,0.0001627595,0.00001114569,0.00001174687,0.00001363002,0.00001613561,0.9868513,0.000298072,0.001844037,0.0001626465,0.01060052],"study_design_scores_gemma":[0.000005686165,0.000009605672,0.00004053595,0.00000258027,0.000002918808,0.000003533679,0.000004029785,0.9990755,0.0001011981,0.0005863206,0.0001662199,0.000001844352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04681073,0.0003518307,0.9458892,0.0001385994,0.00004132196,0.0001015017,0.00009104422,0.000486573,0.006089183],"genre_scores_gemma":[0.5544308,0.000308873,0.4393107,0.00008449407,0.00003361054,0.0005026912,0.0002819288,0.000128049,0.004918847],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02060697,"threshold_uncertainty_score":0.04097402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01682964436848285,"score_gpt":0.2254675953036649,"score_spread":0.2086379509351821,"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."}}