{"id":"W4380143052","doi":"10.4236/cn.2023.152004","title":"A Meta-Learning Approach for Aircraft Trajectory Prediction","year":2023,"lang":"en","type":"article","venue":"Communications and Network","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Trajectory; Aviation; Mean squared error; Aviation safety; Aviation accident; Air traffic management; Random forest; Air traffic control; Data pre-processing; Data mining; Machine learning; Artificial intelligence; Statistics; Engineering; Mathematics","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":[0.0002174935,0.0000625789,0.00009454432,0.00004591289,0.0002462291,0.00002360935,0.0001284439,0.00003518893,0.000007365953],"category_scores_gemma":[0.000004198401,0.00006168504,0.00004301158,0.0002643215,0.00002354232,0.00006060099,0.00004974848,0.00008173332,0.000002902451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007498559,"about_ca_system_score_gemma":0.000002050114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.33554e-7,"about_ca_topic_score_gemma":0.000002683218,"domain_scores_codex":[0.9996521,0.00002538917,0.0001044168,0.0000714267,0.00003409138,0.0001125279],"domain_scores_gemma":[0.9995981,0.00008049714,0.00001481379,0.000270547,0.0000144704,0.0000216027],"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":[0.000001390382,0.000006102659,0.0001323027,0.00002967743,0.0002222093,1.962304e-8,0.0001520293,0.9762258,0.000002588933,0.0009340197,0.01287606,0.009417801],"study_design_scores_gemma":[0.00009313034,0.00001234833,0.0003921189,0.000003498986,0.0002028407,1.705622e-7,0.00006983566,0.9240115,5.337615e-7,0.00007899544,0.07508074,0.00005428126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001188004,0.008128015,0.9670898,0.0002249497,0.0001486032,0.0006218796,0.000009670949,0.00201736,0.02057176],"genre_scores_gemma":[0.8492474,0.01549951,0.1310662,0.00003800165,0.0002129451,0.0008713532,0.001106066,0.0000530295,0.001905442],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8480594,"threshold_uncertainty_score":0.2515443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05313249069463175,"score_gpt":0.2366689409755277,"score_spread":0.183536450280896,"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."}}