Innovative uses of vegetated drainage ditches for reducing agricultural runoff
Bibliographic record
Abstract
Vegetated agricultural ditches play an important role in mitigation of pesticides following irrigation and storm runoff events. In a simulated runoff event in the Mississippi (USA) Delta, the mitigation capacity of a drainage ditch using the pyrethroid esfenvalerate (Asana XL) was evaluated. The pesticide was amended to soil prior to the runoff event to simulate actual runoff, ensuring the presence of esfenvalerate in both water and suspended particulate phases. Water, sediment, and plant samples were collected temporally and spatially along the drainage ditch. Even with mixing of the pesticide with soil before application, approximately 99% of measured esfenvalerate was associated with ditch vegetation (Ludwigia peploides, Polygonum amphibium, and Leersia oryzoides) three hours following event initiation. This trend continued for the 112 d study duration. Simple modeling results also suggest that aqueous concentrations of esfenvalerate could be mitigated to 0.1% of the initial exposure concentration within 510 m of a vegetated ditch. Observed field half-lives in water, sediment, and plant were 0.12 d, 9 d, and 1.3 d, respectively. These results validate the role vegetation plays in the mitigation of pesticides, and that ditches are an indispensable component of the agricultural production landscape.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".