ArticleACS ES&T water2024
Performance of Conditional Random Forest and Regression Models at Predicting Human Fecal Contamination of Produce Irrigation Ponds in the Southeastern United States.
Article in ACS ES&T water, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Large-volume sampling for improved characterization of microbial contamination in agricultural waters: a case study from southeastern U.S. produce farms.Journal of water and health · 2026Article
- Associations between Fecal Contamination of the Household Environment and Enteric Pathogen Detection in Children Living in Maputo, Mozambique.Environment & health (Washington, D.C.) · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Irrigating fresh produce with contaminated water contributes to the burden of foodborne illness. Identifying fecal contamination of irrigation waters and characterizing fecal sources and associated environmental factors can help inform fresh produce safety and health hazard management. Using two previously collected data sets, we developed and evaluated the performance of logistic regression and conditional random forest models for predicting general and human-specific fecal contamination of ponds in southwest Georgia used for fresh produce irrigation. Generic
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.