Seasonal Variation of Herbicide Concentrations in Prairie Farm Dugouts
Bibliographic record
Abstract
Prairie farm dugouts are frequently constructed for use as potable water sources. Consequently, cumulative pesticide inputs via atmospheric deposition and surface runoff may constitute a risk to human health. Since, relative to other pesticides, herbicides are used in greatest amount on the Canadian prairies, herbicide concentrations were intensively monitored in three dugouts over three growing seasons. Herbicides were detected in the water of all three dugouts each growing season which may reflect cumulative inputs from atmospheric and surface processes over the lifetimes of the dugouts, which varied from 11 to 22 yr. Detections, which were not continuous, tended to be seasonal in nature. During the 3-yr study, detections were most frequent during the spring application period and late fall following dugout turnover. Between these periods, herbicide concentrations generally decreased to below detection limits. The reappearance of herbicides in the dugout water during fall turnover and in concentrations generally greater than those present during the spring application period suggest that, under appropriate environmental conditions, the bottom sediments may act as a source of herbicides to the water column. In general, herbicide inputs due to deposition of application drift did not result in detectable concentrations of herbicides in the dugouts. In the only year that winter samples were monitored, herbicides were also detected during ice cover. On the basis of monthly sampling over each growing season, median concentrations of 9 of the 10 herbicides monitored were less than 0.05 microg L(-1). The exception, 2,4-D, which has been used extensively on the Canadian prairies for more than 50 yr and in greatest amounts, was the most frequently detected herbicide. In no case did herbicide concentrations exceed Canadian drinking water guidelines; however, on occasion maximum herbicide concentrations did exceed aquatic life and irrigation water guidelines.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".