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Performance of Conditional Random Forest and Regression Models at Predicting Human Fecal Contamination of Produce Irrigation Ponds in the Southeastern United States

CITE Title : Performance of Conditional Random Forest and Regression Models at Predicting Human Fecal Contamination of Produce Irrigation Ponds in the Southeastern United States Personal Author(s) : Hofstetter, Jessica;Holcomb, David A.;Kahler, Amy M.;Rodrigues, Camila;da Silva, Andre Luiz Biscaia Ribeiro;Mattioli, Mia C.; Published Date : 11 27 2024;11-27-; Source : ACS ES T Water. 4(12):5844-5855 URL : https://stacks.cdc.gov/view/cdc/175258 Hofstetter, Jessica et al. (2024). Performance of Conditional Random Forest and Regression Models at Predicting Human Fecal Contamination of Produce Irrigation Ponds in the Southeastern United States. 4(12). Hofstetter, Jessica et al. "Performance of Conditional Random Forest and Regression Models at Predicting Human Fecal Contamination of Produce Irrigation Ponds in the Southeastern United States" 4, no. 12 (2024) Hofstetter, Jessica et al. "Performance of Conditional Random Forest and Regression Models at Predicting Human Fecal Contamination of Produce Irrigation Ponds in the Southeastern United States" vol. 4, no. 12, 2024 Export RIS Citation Information.

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Hofstetter, Jessica ;

Holcomb, David A.

11 27 2024 | ACS ES T Water. 4(12):5844-5855

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Irrigating fresh produce with contaminated water contributes to the burden of foodborne illness. Identifying fecal contamination of irrigation waters ...

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