Phytoremediation of groundwater contaminated with pesticides using short-rotation willow crops: A case study of an apple orchard
Bibliographic record
Abstract
The occurrence of pesticides in groundwater represents an important health issue, notably for population whose drinking water supply source is located in agricultural areas. However, few solutions have been considered with regard to this issue. We tested the efficacy of a vegetal filtering system made of shrub willows planted at a high density (16,000 plants ha(-1)) to filter or degrade pesticides found in the groundwater flowing out of an apple orchard. Ethylene urea (EU), ethylene thiourea (ETU), tetrahydrophthalimide (THPI), atrazine, and desethylatrazine were monitored in the soil solution in willow and control plots over one growing season. ETU and atrazine concentrations were lower in the willow plots relative to the control plots, whereas desethylatrazine concentration was higher in the willow plots. No significant difference was detected for EU and THPI. Furthermore, pesticide concentrations displayed complex temporal patterns. These results suggest that willow filter systems can filter or degrade pesticides, notably ETU and atrazine, and could be used for phytoremediation purposes. Yet, this potential remains to be quantified with further studies using experimental settings allowing more estimation in time and space.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".