{"id":"W2757860680","doi":"10.1109/tits.2017.2742359","title":"Deconflicted Air-Traffic Planning With Speed-Dependent Fuel-Consumption Formulation","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Fuel efficiency; Air traffic control; Collision avoidance; Routing (electronic design automation); Separation (statistics); Air traffic management; Flight planning; Operations research; Mathematical optimization; Transport engineering; Engineering; Collision; Computer science; Automotive engineering; Computer network; Computer security; Aerospace engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008095356,0.001210791,0.001247573,0.0005794198,0.0003202763,0.001225731,0.00193308,0.001331258,0.004009027],"category_scores_gemma":[0.001511958,0.0005009314,0.0007421449,0.00101625,0.0007149261,0.0008755595,0.0008853012,0.001320578,0.0003464069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001222278,"about_ca_system_score_gemma":0.001847584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008201932,"about_ca_topic_score_gemma":0.007451729,"domain_scores_codex":[0.9992799,0.0002215913,0.00003212463,0.0001577341,0.0001921395,0.0001164208],"domain_scores_gemma":[0.999221,0.0003967243,0.00009972844,0.00005321701,0.0001699732,0.00005940276],"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":[0.00001533115,0.00002009748,0.0001037471,0.00003497967,0.000006392179,0.00005270452,0.000009063191,0.9905046,0.0001680168,0.005824888,0.0003470489,0.00291317],"study_design_scores_gemma":[0.000007150691,0.00002186922,0.00006329614,0.000004705078,0.000003711092,0.00001144381,0.000008973193,0.9968696,0.0001569879,0.002313616,0.0005362558,0.000002380959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04979453,0.0007165454,0.9189485,0.0005328032,0.0001517159,0.0002737285,0.0008434582,0.0002194977,0.02851929],"genre_scores_gemma":[0.8155794,0.0006769252,0.162725,0.000177084,0.0001096434,0.0004852751,0.0008539958,0.0001148336,0.01927778],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008201932,"threshold_uncertainty_score":0.01630837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03055508975252228,"score_gpt":0.2540388994169931,"score_spread":0.2234838096644709,"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."}}