Spatial Influence of Spraying Applications on Water Quality: The Case of the Gibeault-Delisle Watershed (Quebec)
Bibliographic record
Abstract
The Gibault-Delisle watershed (19 km2) is located in the South-West region of the St Lawrence River, Quebec (Canada). It is mainly occupied by horticultural crops. A recent study revealed that the river was contaminated with pesticides all along the cropping season. Some concentration peaks were not linked to rain episodes and could not be imputed to the leaching of deposits on plants or ground. The objectives of this study were to evaluate if measured concentrations could result from drift during applications and to evaluate the potential of some mitigation measures. A spatio-temporal model was built from drift curves and a transport equation along the streams in the watershed. Model inputs were applied pesticides (depending on the crops), wind direction, size and position of the plot with respect to watercourses and water flowrates. Pesticide application records available from a farmer consortium were used to compute maximum hourly concentrations per week. Results for concentration in water from the model are of the same order of magnitude than measurements so drift could be a main contributor to river contamination. Improving sprayer setup to limit drift decreases concentrations by a factor of 10 while implementing systematic 5m buffer zones yielded a 30% reduction. Monte-Carlo simulations based on a probabilistic description of the input factors of the model was performed for a sensitivity analysis and showed that the river flowrate is the main factor influencing the results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".