{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001102105,0.001087961,0.002312666,0.001099426,0.0009508228,0.002503199,0.002036867,0.002836718,0.01420626],"category_scores_gemma":[0.004739355,0.0008836426,0.0009645251,0.001531327,0.001081522,0.00286867,0.001874954,0.00127831,0.0006512931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001659009,"about_ca_system_score_gemma":0.001763928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02497242,"about_ca_topic_score_gemma":0.01228338,"domain_scores_codex":[0.9992336,0.0003368379,0.00003004641,0.0001437614,0.00007675195,0.0001790779],"domain_scores_gemma":[0.9979249,0.001346744,0.0002366567,0.00008463141,0.0002211138,0.0001859096],"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":[0.0002557743,0.00006282783,0.001265299,0.00019115,0.00006411241,0.000304161,0.00006365967,0.9682884,0.0004533891,0.01527395,0.007521734,0.006255493],"study_design_scores_gemma":[0.00003408247,0.00004540082,0.0003344849,0.0000133637,0.00001715609,0.00005601576,0.00009286052,0.9917095,0.0001439055,0.006900598,0.0006427358,0.000009929126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5438805,0.002934991,0.4001351,0.006589463,0.0003470953,0.0004628157,0.006127934,0.0006851124,0.03883709],"genre_scores_gemma":[0.9565144,0.0007230177,0.02451463,0.000194081,0.0001052636,0.000152208,0.001794401,0.0001364921,0.01586547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02497242,"threshold_uncertainty_score":0.04965413,"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."}}