ArticleFrontiers in sustainable food systems2020
Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production.
Article in Frontiers in sustainable food systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
12 citing papers in PubMed.
- Artificial intelligence in food safety.NPJ science of food · 2026Review
- Performance of Conditional Random Forest and Regression Models at Predicting Human Fecal Contamination of Produce Irrigation Ponds in the Southeastern United States.ACS ES&T water · 2024Article
- Scale of analysis drives the observed ratio of spatial to non-spatial variance in microbial water quality: insights from two decades of citizen science data.Journal of applied microbiology · 2023Article
- Conditional Forest Models Built Using Metagenomic Data Accurately PredictedMicrobiology spectrum · 2023Article
- Salmonella Prevalence Is Strongly Associated with Spatial Factors while Listeria monocytogenes Prevalence Is Strongly Associated with Temporal Factors on Virginia Produce Farms.Applied and environmental microbiology · 2023Article
- Structural Equation Models Suggest That On-Farm Noncrop Vegetation Removal Is Not Associated with Improved Food Safety Outcomes but Is Linked to Impaired Water Quality.Applied and environmental microbiology · 2022Article
- Correlation value determined to increase Salmonella prediction success of deep neural network for agricultural waters.Environmental monitoring and assessment · 2022Article
- Humans and Hoofed Livestock Are the Main Sources of Fecal Contamination of Rivers Used for Crop Irrigation: A Microbial Source Tracking Approach.Frontiers in microbiology · 2022Article
- Integrative Survey of 68 Non-overlapping Upstate New York Watersheds Reveals Stream Features Associated With Aquatic Fecal Contamination.Frontiers in microbiology · 2021Article
- Comparison of Resampling Algorithms to Address Class Imbalance when Developing Machine Learning Models to Predict Foodborne Pathogen Presence in Agricultural Water.Frontiers in environmental science · 2021Article
- Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to PredictFrontiers in artificial intelligence · 2021Article
- Spatiotemporal trends of Escherichia coli levels and their influences vary among ponds in the coastal plain of Georgia.Journal of environmental qualityArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
While the Food Safety Modernization Act established standards for the use of surface water for produce production, water quality is known to vary over space and time. Targeted approaches for identifying hazards in water that account for this variation may improve growers' ability to address pre-harvest food safety risks. Models that utilize publicly-available data (e.g., land-use, real-time weather) may be useful for developing these approaches. The objective of this study was to use pre-existing datasets collected in 2017 (
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Registered trials
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