Application of Mathematical Modeling to Determine The Size of On-Site Grass Filters for Reducing Farm Pesticide Pollution
Bibliographic record
Abstract
This paper presents a mathematical approach for estimating the size of a grass filter area for removing pesticide residues from agricultural drainage waters. The method utilizes a water table management model, DRAINMOD, for simulating drainage waters from agricultural land and then a solute transport model, PRZM2, for simulating pesticide concentrations in drain effluent discharging from grass filters. DRAINMOD was used to estimate daily drain outflows that occurred in a 100 ha subsurface drained field in the Ottawa-St. Lawrence lowlands by running the model for a one-in-twenty year annual rainfall period. Atrazine (AZ), metolachlor (MT) and metribuzin (MZ) are the most common herbicides that are found in drainage waters. The simulated drain outflows were assumed to contain 50 µg/l of AZ, MT and MZ residues, and simulations were carried out with PRZM2 to determine the required size of grass filter area to make drainage waters safer for aquatic life and a marine habitat. It was found that no more than 6% of the farm area could be used to reduce the concentrations in drainage waters from 50 µg/l to less than 1 µg/l for the three herbicides.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".