{"id":"W4318147664","doi":"10.1109/bigdata55660.2022.10021035","title":"Analysis of Airfare during Pandemic: A Multi-Agent Based Modeling Approach","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Big Data (Big Data)","topic":"Aviation Industry Analysis and Trends","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Air travel; Computer science; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Aviation; Business; Risk analysis (engineering); Operations research; Engineering; Aerospace engineering; Infectious disease (medical specialty)","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001046469,0.0002174926,0.0005494365,0.001170319,0.0002611984,0.0001147398,0.0030815,0.00007971544,0.002740691],"category_scores_gemma":[0.0001389254,0.000259064,0.0001616261,0.001073116,0.00004230458,0.0003936583,0.001287143,0.0004572174,0.00003979107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001950845,"about_ca_system_score_gemma":0.00009098103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000648751,"about_ca_topic_score_gemma":0.0002193735,"domain_scores_codex":[0.9971802,0.00007012166,0.0009817684,0.0011808,0.0003456498,0.0002414282],"domain_scores_gemma":[0.9968229,0.00004574241,0.0007278241,0.002211212,0.0001032307,0.00008909771],"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":[0.0002489206,0.002001665,0.2289888,0.0000557394,0.006536653,0.00002586348,0.000331167,0.7233828,0.0002463778,0.01946663,0.002376601,0.01633884],"study_design_scores_gemma":[0.0006956338,0.00002063014,0.005638995,0.000008382227,0.0001789713,0.000001696167,0.0003065174,0.9881675,0.00001245937,0.0001003481,0.00460632,0.0002625363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2426676,0.0003365726,0.4804563,0.001195283,0.003210128,0.0003527333,0.2627497,0.0001081361,0.008923583],"genre_scores_gemma":[0.9484544,0.00009828261,0.0005296575,0.0001470984,0.0001882397,0.00004772185,0.04987683,0.0000188988,0.0006388735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7057868,"threshold_uncertainty_score":0.9999862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5808910784080009,"score_gpt":0.3499821377569418,"score_spread":0.2309089406510591,"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."}}