{"id":"W3116699933","doi":"10.1111/tbed.13973","title":"Geospatial dynamics of COVID‐19 clusters and hotspots in Bangladesh","year":2021,"lang":"en","type":"article","venue":"Transboundary and Emerging Diseases","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Adidas (Canada)","funders":"","keywords":"Geography; Poisson regression; Case fatality rate; Scan statistic; Demography; Spatial analysis; Statistics; Coronavirus disease 2019 (COVID-19); Poisson distribution; Geospatial analysis; Population; Spatial epidemiology; Cluster (spacecraft); Epidemiology; Cartography; Medicine; Infectious disease (medical specialty); Mathematics; Disease; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0002237568,0.0001569952,0.0004174531,0.00005911417,0.0001664426,0.00002073209,0.00006725288,0.00005799288,0.0001086958],"category_scores_gemma":[0.001157471,0.0001405623,0.00007670392,0.0001761252,0.0003147354,0.0000803455,0.00006823557,0.0001038963,3.68183e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000500007,"about_ca_system_score_gemma":0.0001126152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002813331,"about_ca_topic_score_gemma":0.001776305,"domain_scores_codex":[0.9988453,0.0001562594,0.0003394769,0.0003018415,0.0001305557,0.000226532],"domain_scores_gemma":[0.9982499,0.001339459,0.00006303778,0.0001432976,0.00002790125,0.0001763498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008905251,0.000795797,0.9133359,0.006070911,0.0002921225,0.0004036491,0.00713832,0.0003867419,0.0001122204,0.0453341,0.001140379,0.02409936],"study_design_scores_gemma":[0.003688799,0.000264521,0.5745789,0.0003972664,0.000493154,0.00002881272,0.004045112,0.009815717,0.00003023491,0.4013286,0.004532055,0.0007968892],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857298,0.004697448,0.004030643,0.004966336,0.00006623751,0.0001478763,0.0001842827,0.00005420595,0.000123179],"genre_scores_gemma":[0.997345,0.00116971,0.0007548017,0.0006044754,0.00002621072,0.00001315701,0.00002871847,0.00001133111,0.00004655364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3559945,"threshold_uncertainty_score":0.5731963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07534808998943374,"score_gpt":0.366610790206271,"score_spread":0.2912627002168373,"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."}}