Effect of Harsh or Mild Extraction of Soil on Pesticide Leaching to Groundwater
Bibliographic record
Abstract
Assessment of leaching to groundwater is an important aspect of pesticide risk assessment. The first leaching tier usually consists of simulations with leaching scenarios based on pesticide-soil properties derived from laboratory studies. Because the extractability of pesticide residues in such studies decreases with time, the harshness of the extraction method influences these pesticide-soil properties. This study investigates the effect of using a mild or harsh extraction method on simulated leaching to groundwater with consideration of substances with a range of half-lives and organic matter sorption coefficient values for selected leaching scenarios. The model for linking the concentrations of the mild and the harsh systems was based on laboratory studies with two pesticides and a Dutch sandy soil and was tested against Canadian field studies with atrazine (6-chloro-2-ethyl-4-isopropyl-1,3,5-triazine-2,4-diamine). The degradation rate and the aged-sorption parameters of each "mild" soil-substance system were derived from a hypothetical laboratory incubation study using prescribed parameter values for the corresponding "harsh" soil-substance system. Simulations were performed for three European leaching scenarios (United Kingdom, France, Portugal). For the best-guess parameter set, the leaching concentrations of the harsh system were approximately equal to those of the mild system at leaching concentrations greater than 1 μg L and were at most approximately a factor of two higher than those of the mild systems at mild leaching concentrations between 0.01 and 0.1 μg L. However, an extreme parameter set led to harsh leaching concentrations that were at most approximately 10 times higher than the mild leaching concentrations at levels between 0.01 and 0.1 μg L.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".