Vegetated agricultural drainage ditches for the mitigation of pyrethroid-associated runoff
Bibliographic record
Abstract
Drainage ditches are indispensable components of the agricultural production landscape. A benefit of these ditches is contaminant mitigation of agricultural storm runoff. This study determined bifenthrin and lambda-cyhalothrin (two pyrethroid insecticides) partitioning and retention in ditch water, sediment, and plant material as well as estimated necessary ditch length required for effective mitigation. A controlled-release runoff simulation was conducted on a 650-m vegetated drainage ditch in the Mississippi Delta, USA. Bifenthrin and lambda-cyhalothrin were released into the ditch in a water-sediment slurry. Samples of water, sediment, and plants were collected and analyzed for pyrethroid concentrations. Three hours following runoff initiation, inlet bifenthrin and lambda-cyhalothrin water concentrations ranged from 666 and 374 microg/L, respectively, to 7.24 and 5.23 microg/L at 200 m downstream. No chemical residues were detected at the 400-m sampling site. A similar trend was observed throughout the first 7 d of the study where water concentrations were elevated at the front end of the ditch (0-25 m) and greatly reduced by the 400-m sampling site. Regression formulas predicted that bifenthrin and lambda-cyhalothrin concentrations in ditch water were reduced to 0.1% of the initial value within 280 m. Mass balance calculations determined that ditch plants were the major sink and/or sorption site responsible for the rapid aqueous pyrethroid dissipation. By incorporating vegetated drainage ditches into a watershed management program, agriculture can continue to decrease potential non-point source threats to downstream aquatic receiving systems. Overall results of this study illustrate that aquatic macrophytes play an important role in the retention and distribution of pyrethroids in vegetated agricultural drainage ditches.
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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".