{"id":"W3194357836","doi":"10.5383/jttm.03.02.001","title":"Regionalization for urban air mobility application in metropolitan areas: case studies in San Francisco and New York","year":2021,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Metropolitan area; Geography; Identification (biology); Population; Cluster analysis; Cartography; Transport engineering; Computer science; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001988394,0.0001066847,0.0001626945,0.0002921562,0.00002260865,0.00002104858,0.00006313736,0.00003591776,0.000005824779],"category_scores_gemma":[0.00001053845,0.0001124588,0.00003683695,0.0001886822,0.00002769758,0.0002133246,0.000004041925,0.00006441581,1.79378e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001347422,"about_ca_system_score_gemma":0.00001228198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001127371,"about_ca_topic_score_gemma":0.000808946,"domain_scores_codex":[0.9990758,0.00001564044,0.0004839854,0.0001472468,0.0001859432,0.00009137184],"domain_scores_gemma":[0.9996215,0.00004654701,0.0001006243,0.00005502096,0.0001329006,0.00004336895],"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":[0.0001291979,0.0001493878,0.006452926,0.0004091668,0.0003825155,0.0005698335,0.002780257,0.9261965,0.00000727714,0.0194149,0.001380805,0.04212721],"study_design_scores_gemma":[0.01701732,0.0002792543,0.1839562,0.001316696,0.0005914637,0.0004567782,0.04387578,0.7283794,0.000160224,0.006234686,0.01666807,0.001064142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7080629,0.003725175,0.286678,0.0005109339,0.0003677191,0.0004431647,0.00001407822,0.00003752622,0.0001604323],"genre_scores_gemma":[0.9925669,0.001399073,0.005736569,0.00005414429,0.00007358401,0.0000211012,0.00007497342,0.00001137736,0.0000622364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.284504,"threshold_uncertainty_score":0.4585938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02188841644710822,"score_gpt":0.2702549499012892,"score_spread":0.248366533454181,"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."}}