Incorporating the benefits of vegetative filter strips into risk assessment and risk management of pesticides
Bibliographic record
Abstract
The pesticide registration process in North America, including the USA and Canada, involves conducting a risk assessment based on relatively conservative modeling to predict pesticide concentrations in receiving waterbodies. The modeling framework does not consider some commonly adopted best management practices that can reduce the amount of pesticide that may reach a waterbody, such as vegetative filter strips (VFS). Currently, VFS are being used by growers as an effective way to reduce off-site movement of pesticides, and they are being required or recommended on pesticide labels as a mitigation measure. Given the regulatory need, a pair of multistakeholder workshops were held in Raleigh, North Carolina, to discuss how to incorporate VFS into pesticide risk assessment and risk management procedures within the North American regulatory framework. Because the risk assessment process depends heavily on modeling, one key question was how to quantitatively incorporate VFS into the existing modeling approach. Key outcomes from the workshops include the following: VFS have proven effective in reducing pesticide runoff to surface waterbodies when properly located, designed, implemented, and maintained; Vegetative Filter Strip Modeling System (VFSMOD), a science-based and widely validated mechanistic model, is suitable for further vetting as a quantitative simulation approach to pesticide mitigation with VFS in current regulatory settings; and VFSMOD parametrization rules need to be developed for the North American aquatic exposure assessment. Integr Environ Assess Manag 2024;20:454-464. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".