Assessment of insecticide risk quantification methods: Introducing the Pesticide Risk Tool and its improvements over the Environmental Impact Quotient
Bibliographic record
Abstract
Abstract Tools for quantifying nontarget pesticide risks have long been used for documenting the benefits of Integrated Pest Management (IPM) programs. One resource receiving little attention is the Pesticide Risk Tool (PRT), developed by the IPM Institute in Madison, WI. The PRT includes 15 indices and uses a probabilistic approach to assess the risk for the environmental and human health effects of insecticides, fungicides, and herbicides. In this article, we compare the PRT to the Environmental Impact Quotient (EIQ) to highlight the PRT’s approach to characterizing risk and several improvements over the EIQ. Comparing the calculated risk scores between the EIQ and PRT shows a similar trend with organophosphate insecticides, usually reflecting the highest toxicity risks, with more pronounced differences for pyrethroids and neonicotinoids, but exact toxicity rankings differ. Advantages of the PRT over the EIQ include the probabilistic approach to quantify risk and reliance on field impact data where available, the use of raw data for inputs versus a scoring system, correction of known issues with the EIQ, and its greater diversity of risk indices. Some disadvantages of the PRT include its lack of data on discontinued products, the absence of a total risk score, use of different scoring scales between indices, and its cost. However, given the pros and cons of each method, we believe the PRT to be a useful tool for researchers, extension professionals, and growers who wish to account for environmental and human health risks when building IPM programs.
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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 | 0.045 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".