Mobilization of pesticides on an agricultural landscape flooded by a torrential storm
Bibliographic record
Abstract
Mobilization of pesticides into surface waters of flooded agricultural landscapes following extreme precipitation events has not been previously investigated. After receiving 96 mm of rain in the previous 45 d, the Vanguard area of southeastern Saskatchewan, Canada, was subjected to a torrential storm on July 3, 2000, that produced as much as 375 mm of rain in 8 h. The majority of herbicides, but no insecticides, would have been applied to crops in the Vanguard area during the four weeks preceding the storm. After the storm, 19 herbicides and insecticides were detected in flooded wetlands, with 14 of them detected in 50% or more of wetlands. Average concentrations ranged from 0.43 ng/L (endosulfan) to 362 ng/L (2,4-dichlorophenoxyacedic acid). The pesticides probably were from long-range transport, followed by deposition in rain, and from herbicides applied to crops within the area subjected to the storm (1,700 km2). In the following year, when only 62 mm of rain fell in the same 45 d, only five pesticides were detected in 50% or more of wetlands. We estimated that for the 1,700-km2 storm zone, 278 kg of herbicide were mobilized into rain and by runoff into surface waters, and 105 kg were removed from the Vanguard area by discharge into Notukeu Creek. Significant quantities of herbicides are mobilized to aquatic environments when prairie agricultural landscapes are subjected to torrential storms. In these circumstances, flooded wells and small municipal reservoirs used as sources of drinking water may be compromised by 10 or more pesticides, some at relatively high concentrations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".