{"id":"W2594920293","doi":"10.1287/trsc.2016.0714","title":"Solving the Air Conflict Resolution Problem Under Uncertainty Using an Iterative Biobjective Mixed Integer Programming Approach","year":2017,"lang":"en","type":"article","venue":"Transportation Science","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"Agence Nationale de la Recherche; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Mathematical optimization; Integer programming; Monte Carlo method; Computer science; Integer (computer science); Pareto principle; Set (abstract data type); Resolution (logic); Graph; Mathematics; Theoretical computer science; Artificial intelligence","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":[0.002528081,0.00150822,0.001419038,0.00102782,0.0005595976,0.001489064,0.001854357,0.001779329,0.001876797],"category_scores_gemma":[0.005077391,0.0008591265,0.001289373,0.00098028,0.0009804112,0.001213306,0.001530391,0.001568976,0.0002101899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038385,"about_ca_system_score_gemma":0.001855752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004837001,"about_ca_topic_score_gemma":0.003517246,"domain_scores_codex":[0.9986728,0.0006846844,0.0000518541,0.0001758743,0.000261608,0.0001531172],"domain_scores_gemma":[0.995945,0.003419422,0.0002881584,0.00006450375,0.0002119736,0.00007100686],"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":[0.00001505817,0.00001983293,0.0001552605,0.00003795395,0.00002300217,0.00003810192,0.00002897124,0.9891005,0.0001960887,0.004835456,0.0001249772,0.00542479],"study_design_scores_gemma":[0.000005574615,0.00001643665,0.00002759696,0.000006321457,0.000005507189,0.000008198428,0.00001170413,0.9966343,0.0001147281,0.003008726,0.0001581566,0.000002763142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01347575,0.0002252147,0.9828308,0.0002520253,0.00002243135,0.00006921859,0.0000447838,0.00008432595,0.002995535],"genre_scores_gemma":[0.425644,0.0004055407,0.5697886,0.0002663651,0.00007431287,0.0006485496,0.0001764711,0.0001115172,0.002884617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004837001,"threshold_uncertainty_score":0.01336998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04253927127099312,"score_gpt":0.281123372281897,"score_spread":0.2385841010109039,"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."}}