{"id":"W4407699929","doi":"10.1201/9781003618140-167","title":"COVID-19'S Impact on Underplanning: A Bibliometric Analysis","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Geography; Virology; Medicine; Outbreak","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.006696882,0.0006404543,0.002174698,0.08570928,0.001672219,0.005619794,0.001211167,0.001201334,0.01116631],"category_scores_gemma":[0.07416252,0.0003076739,0.002854002,0.1791415,0.001175988,0.00426012,0.002904395,0.001267832,0.001600534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002720393,"about_ca_system_score_gemma":0.006971998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02120013,"about_ca_topic_score_gemma":0.02326997,"domain_scores_codex":[0.9889036,0.002361152,0.002682936,0.001321939,0.003903654,0.0008266747],"domain_scores_gemma":[0.889209,0.07755888,0.01679903,0.002258915,0.01170032,0.002473932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007492332,0.0002565032,0.6360586,0.05494698,0.004683199,0.0008427753,0.004440289,0.00159448,0.000422258,0.005806427,0.09542627,0.194773],"study_design_scores_gemma":[0.0001392462,0.0001712747,0.805798,0.01079479,0.005552084,0.001246056,0.01009855,0.00302329,0.0005264034,0.004101578,0.1583138,0.0002349587],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4053266,0.1477996,0.002318155,0.01539321,0.0007433442,0.0008200789,0.380291,0.0007067661,0.04660128],"genre_scores_gemma":[0.8298188,0.07094625,0.003659365,0.001221374,0.0009901974,0.0009782956,0.08841536,0.0002265948,0.003743822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9142907,"threshold_uncertainty_score":0.04215342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3816963937399538,"score_gpt":0.5041163794986205,"score_spread":0.1224199857586667,"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."}}