{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003252684,0.0001868743,0.0002535211,0.00231828,0.0004425535,0.0009155296,0.0003575655,0.0002554057,0.001886071],"category_scores_gemma":[0.001694476,0.000180814,0.0002309454,0.004066785,0.0004667473,0.0005436176,0.0009081712,0.0001846613,0.0003368752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001182476,"about_ca_system_score_gemma":0.0006779275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06565358,"about_ca_topic_score_gemma":0.06370977,"domain_scores_codex":[0.9995229,0.0001178638,0.00005564524,0.0001222471,0.00008467563,0.00009655773],"domain_scores_gemma":[0.9990444,0.0001787898,0.0003688366,0.00006967856,0.0002026418,0.0001355792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001185067,0.00002230838,0.9819238,0.00006932343,0.00005973415,0.0004773091,0.002247875,0.00273333,0.001335517,0.0008491206,0.001221106,0.008942132],"study_design_scores_gemma":[0.000007202606,0.00004265321,0.9875588,0.00003252442,0.00002561594,0.0002820067,0.005749265,0.003471851,0.0001710769,0.0003667335,0.002260313,0.00003188193],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953276,0.0001774769,0.0003068937,0.00009768787,0.00000346011,0.00002818158,0.002174596,0.00002153327,0.001862624],"genre_scores_gemma":[0.9981688,0.0001187251,0.0002590423,0.000009099047,0.000001943141,0.00001848879,0.001081253,0.000004015164,0.0003386542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06565358,"threshold_uncertainty_score":0.1305429,"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."}}