Low detection of glyphosate in rivers following application in forestry
Bibliographic record
Abstract
BACKGROUND: Glyphosate is the most commonly used herbicide in the world, and is used in agriculture, forestry, and urban settings. In regions with high glyphosate use, such as agricultural, glyphosate and its' major derivative aminomethylphosphonic acid (AMPA) are frequently detected in surface waters. In Canadian forestry glyphosate-based herbicides are used to control vegetation that competes with conifer trees and are applied one to two times during a rotation, leading to infrequent application to the same area. Forestry occurs over a large spatial extent, and the cumulative application in space can lead to a large percentage of the land base receiving an application through time. To assess the frequency and concentration of glyphosate and AMPA in surface waters of a region where forestry is the dominant use sector, we conducted three monitoring programs targeting: (i) immediately after application, (ii) after rainfall, and (iii) cumulative application over a large spatial extent. RESULTS: Across all monitoring programs we collected 296 water samples between August and October from eight river systems over two years and detected glyphosate in one sample at 17 ppb. CONCLUSION: Glyphosate is not likely present in surface waters during baseflow conditions as a result of applications in forestry. Lack of detection is likely because soil capacity to bind glyphosate remains high due to infrequent applications to the same area, and factors that limit sediment transport to surface waters such as buffers. Additional sampling is needed during other stream conditions, ideally spring freshet, to determine peak concentrations. © 2023 National Research Council Canada. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Reproduced with the permission of the Minister of Innovation, Science, and Economic Development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".