Automation and evaluation of three pesticide fate models for a national analysis of leaching risk in Canada
Bibliographic record
Abstract
Under the National Agri-Environmental Health Analysis and Reporting Program (NAHARP), pesticide fate models are being used to develop an indicator of risk of water contamination by pesticides (IROWC-Pest) in Canada. The large number of model runs needed for a national analysis of the risk of pesticide leaching to ground water required the development of a computer program, AutoPFM (Automate Pesticide Fate Model), to automate the running of pesticide fate models. Using Manitoba as a test province, and the selected pesticide fate models PRZM, LEACHP, and MACRO, AutoPFM permitted the estimation of the leaching potential of the fourteen most used agricultural pesticides in Manitoba. Assuming an application rate of 300 g ha-1 for each pesticide, only six pesticides demonstrated leaching across most soil series. For these six pesticides, there was significant correlation in how PRZM and LEACHP ranked the leaching potential of 337 Manitoba soil series. Because of its long running times, the estimation of leaching potential with MACRO was limited to two pesticides (2,4-D and MCPA). For these pesticides, MACRO showed significant correlation with the PRZM and LEACHP in ranking the soil series. Based on the results described in this paper, PRZM was chosen as the pesticide leaching model for use in IROWC-Pest. Key words: Risk indicators, pesticide, PRZM, LEACHM, LEACHD, MACRO, model automation, leaching, soil
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".