Machine learning in understanding environmental variability of vibriosis in coastal waters.

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Bibliographic Details
Title: Machine learning in understanding environmental variability of vibriosis in coastal waters.
Authors: Magers BM; Geohealth and Hydrology Laboratory, Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida, USA., Brumfield KD; Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland, USA.; University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland, USA., Kumar S; Geohealth and Hydrology Laboratory, Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida, USA., Colwell RR; Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland, USA.; University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland, USA., Jutla AS; Geohealth and Hydrology Laboratory, Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida, USA.
Source: Applied and environmental microbiology [Appl Environ Microbiol] 2025 Sep 17; Vol. 91 (9), pp. e0071625. Date of Electronic Publication: 2025 Aug 14.
Publication Type: Journal Article; Research Support, U.S. Gov't, Non-P.H.S.
Journal Info: Publisher: American Society for Microbiology Country of Publication: United States NLM ID: 7605801 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1098-5336 (Electronic) Linking ISSN: 00992240 NLM ISO Abbreviation: Appl Environ Microbiol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1098-5336
DOI:10.1128/aem.00716-25