{"id":"W4414033116","doi":"10.1016/j.apgeog.2025.103773","title":"Remaking the smart city through the COVID-19 pandemic: Seoul, Singapore, Taipei","year":2025,"lang":"en","type":"article","venue":"Applied Geography","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Geography; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Socioeconomics; Cartography; Virology; Sociology; Medicine; Outbreak; Infectious disease (medical specialty)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0002931915,0.0002234318,0.0001947946,0.0001149535,0.0005683906,0.0001018687,0.0005904309,0.0001657251,0.00003719669],"category_scores_gemma":[0.0000502203,0.00013597,0.000153459,0.0009653877,0.0004054517,0.00005015934,0.0001792389,0.0004676336,0.00001036625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004360158,"about_ca_system_score_gemma":0.00002322119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002447515,"about_ca_topic_score_gemma":0.0001295796,"domain_scores_codex":[0.9989436,0.00001848785,0.0002334422,0.0002418479,0.0001687169,0.0003938621],"domain_scores_gemma":[0.9987981,0.0003980467,0.00004364856,0.0007104746,0.00001877164,0.00003098155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005708986,0.0000604007,0.2821003,0.0003204035,0.001090182,0.00001371499,0.002117872,0.009295665,0.0009478114,0.497805,0.1625097,0.04368183],"study_design_scores_gemma":[0.0002816706,0.000008952854,0.007412564,0.00001552827,0.00006337815,0.000008354207,0.001782575,0.0002423307,0.0005186923,0.1035581,0.8858634,0.0002444097],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.451377,0.01013479,0.07583526,0.01205297,0.002316368,0.002231438,0.00006253807,0.009684695,0.436305],"genre_scores_gemma":[0.9948561,0.0004589329,0.0004285032,0.003911095,0.00006871678,0.0001827044,0.000008554557,0.00002108994,0.00006435425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7233537,"threshold_uncertainty_score":0.5544697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02607997183836344,"score_gpt":0.2547202654773834,"score_spread":0.22864029363902,"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."}}