Monitoring and Modeling Glyphosate Transport in the Belize River Watershed
Bibliographic record
Abstract
Glyphosate, an effective herbicide used worldwide as a weed control, can be transported from application areas to unintended locations. In this study, we use high performance liquid chromatography (HPLC), enzyme-linked immunosorbent assay (ELISA) kits, and liquid chromatography with tandem mass spectrometry (LC-MS/MS) to quantify concentrations of glyphosate, and the Soil Water Assessment Tool (SWAT) to model transport of glyphosate in the Belize River Watershed. Water samples were collected from two rural communities with rudimentary drinking water systems. Quantification analyses showed that glyphosate was not present in the water samples. The model confirms that glyphosate is not expected to be present in the sampling locations. However, the model did reveal that glyphosate transport to the Belize River may be occurring and identified three subbasins most likely to be at risk due to having the highest percentages of days exceeding the EU standard for glyphosate of 0.1 μg/L. One of these subbasins, located just downstream of the sampling locations, was the most significant contributor of soluble glyphosate to the river (p-values <;0.0). Soluble glyphosate concentrations in this subbasin inflow and outflow exceeded the EU standard by 12.53% and 11.65% of the time, respectively. Additionally, concentrations of glyphosate sorbed to sediment were significantly greater than soluble glyphosate in surface runoff (p-values <;0.0). This work demonstrates a framework for applying SWAT for pesticide transport modeling in developing countries and has the potential to be a powerful and accessible tool for watershed management and measurement of sustainable development progress when monitoring data is unavailable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".