{"id":"W3212339558","doi":"10.1016/j.cor.2021.105611","title":"Skyport location problem for urban air mobility system","year":2021,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Ministry of Land, Infrastructure and Transport","keywords":"Taxis; Computer science; Traffic congestion; Flow network; Scheduling (production processes); Genetic algorithm; Service (business); Heuristic; Mathematical optimization; Operations research; Routing (electronic design automation); Air traffic control; Transport engineering; Computer network; Engineering; Artificial intelligence; 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":[0.0005242263,0.00008697226,0.0001040327,0.0001356015,0.0003058363,0.0001450888,0.0001537232,0.00005406045,0.00001657543],"category_scores_gemma":[0.00002938061,0.00009459199,0.000035097,0.0006310746,0.00003226924,0.0001996806,0.00005847978,0.0001264774,0.00003471627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002080293,"about_ca_system_score_gemma":0.00009551409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001114377,"about_ca_topic_score_gemma":0.00004411856,"domain_scores_codex":[0.9989856,0.0000705384,0.0002197576,0.0002386169,0.0002380637,0.0002474637],"domain_scores_gemma":[0.9988539,0.00006625045,0.000005681734,0.00030439,0.000708867,0.00006094475],"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":[0.000002338357,0.00003968741,0.00003440122,0.0003635095,0.00002384437,0.000002044853,0.0002400034,0.9565591,0.00006688685,0.01236922,0.02679122,0.003507737],"study_design_scores_gemma":[0.0002115497,0.00002539156,0.0002183109,0.00006248231,0.000005335088,0.000001763268,0.0002785174,0.9843369,0.0005111899,0.00001413371,0.0142401,0.0000943057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005631722,0.0001936636,0.9886367,0.0004419585,0.0002399556,0.0009865947,0.000006313538,0.000362363,0.003500775],"genre_scores_gemma":[0.9388875,0.0000268296,0.05857822,0.00003079114,0.0002021093,0.0004135169,0.0003547262,0.00002679576,0.001479479],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9332558,"threshold_uncertainty_score":0.3857349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0330537921794643,"score_gpt":0.2965752524707752,"score_spread":0.2635214602913108,"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."}}