{"id":"W2415545883","doi":"10.1017/aer.2016.37","title":"New method to compute the missed approach fuel consumption and its emissions","year":2016,"lang":"en","type":"article","venue":"The Aeronautical Journal","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Fuel efficiency; Descent (aeronautics); Range (aeronautics); Computation; Atmosphere (unit); Environmental science; Operations research; Computer science; Engineering; Automotive engineering; Meteorology; Aerospace engineering; Algorithm","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":[0.0004145206,0.00008714471,0.00008210919,0.00002741736,0.0001594,0.00006452946,0.0001817844,0.00003379407,0.0001535583],"category_scores_gemma":[0.00003132858,0.00003697369,0.00002840345,0.00007072299,0.00002016446,0.00007056853,0.0000514127,0.0001462815,0.00004416916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000231823,"about_ca_system_score_gemma":0.00001058484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.333306e-7,"about_ca_topic_score_gemma":4.169558e-7,"domain_scores_codex":[0.9993828,0.00007874886,0.0001416593,0.00007616716,0.0001439043,0.0001767528],"domain_scores_gemma":[0.9995113,0.0001727103,0.00001881755,0.00009984805,0.00001986073,0.0001774658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008270483,0.00005428716,0.000123705,0.00006994676,0.0002862181,0.000007809291,0.001428175,0.1012818,0.004681672,0.09453192,0.07987075,0.717581],"study_design_scores_gemma":[0.001287825,0.00006984975,0.005706491,0.00009710967,0.0001419604,0.000208408,0.0001106852,0.9283621,0.0002806739,0.003427872,0.06001539,0.000291635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002414802,0.0003637996,0.9823207,0.01321799,0.0001089192,0.0001409038,7.261395e-7,0.00006778781,0.001364331],"genre_scores_gemma":[0.8326128,0.0006465155,0.1635183,0.0008185312,0.0005149737,0.000006093139,0.000001554597,0.00003328393,0.001847971],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.830198,"threshold_uncertainty_score":0.1681356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03099779382371582,"score_gpt":0.2680786911522751,"score_spread":0.2370808973285593,"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."}}