{"id":"W2678726811","doi":"10.1016/j.cja.2017.06.006","title":"New reference trajectory optimization algorithm for a flight management system inspired in beam search","year":2017,"lang":"en","type":"article","venue":"Chinese Journal of Aeronautics","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Cruise; Trajectory; Trajectory optimization; Algorithm; Computer science; Climb; Search algorithm; Heuristic; Mathematical optimization; Control theory (sociology); Simulation; Engineering; Mathematics; Aerospace engineering; Optimal control","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":[0.000485656,0.0006685666,0.0007045376,0.0006869324,0.0004788805,0.0006233206,0.0009122377,0.0009153499,0.004184745],"category_scores_gemma":[0.000886092,0.0003312041,0.0005409982,0.0009000088,0.0003633882,0.0005834798,0.0006265303,0.0007096268,0.0006806341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006731122,"about_ca_system_score_gemma":0.001225308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007915893,"about_ca_topic_score_gemma":0.006084352,"domain_scores_codex":[0.9998208,0.0000505021,0.00000951146,0.00003774541,0.00005458632,0.00002687364],"domain_scores_gemma":[0.9997762,0.00008865797,0.00002760005,0.00001624133,0.00008001694,0.00001131316],"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":[0.00008899204,0.00004786224,0.0006769799,0.00008827458,0.00004877855,0.00005274405,0.0000757686,0.8708872,0.004253062,0.01315552,0.002564786,0.1080601],"study_design_scores_gemma":[0.00001867399,0.00003152487,0.00009788661,0.000006209392,0.000005679157,0.00001597513,0.000006997271,0.9972469,0.0004037218,0.001084281,0.001077888,0.000004360245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006694511,0.0001201299,0.9906887,0.00005396532,0.00002462672,0.0000517666,0.00004608747,0.0003856093,0.001934574],"genre_scores_gemma":[0.1679671,0.0001957522,0.8269477,0.00009596017,0.00002826203,0.0005312613,0.000347152,0.0001485478,0.003738293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007915893,"threshold_uncertainty_score":0.01573962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01542141620409817,"score_gpt":0.2473798812970901,"score_spread":0.2319584650929919,"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."}}