Evaluating Improvements to Exposure Estimates from Fate and Transport Models by Incorporating Environmental Sampling Effort and Landscape-level Contaminant Use
Bibliographic record
Abstract
A BSTRACT The Pesticide in Water Calculator (PWC) is a fate and transport model used by the Environmental Protection Agency and Health Canada to estimate pesticide exposures in lentic freshwater ecosystems and make pesticide registration decisions. We leverage over 600,000 field measurements of 31 common insecticides and herbicides to test whether incorporating environmental sampling effort (number of times a pesticide is sampled) and landscape-level contaminant use (national application amount) can improve PWC validation and prediction, respectively. We found that maximum measured concentrations of 38% of herbicides and 42% of insecticides exceeded maximum estimated environmental concentrations (EECs) generated by the PWC, suggesting that EECs often do not represent worst-case exposure. For lentic systems, variance in pesticide field measurements explained by EECs increased by 50% when sampling effort was included. For lotic systems, variance explained increased by only 4%, most likely because lotic systems are sampled over 4.9 times as much as lentic systems. Including landscape-level use more than doubled the ability of the PWC to predict maximum pesticides concentrations in lentic systems. Exposure characterization in risk assessment can be improved by including sampling effort in model validation and landscape-level use in predictions, thus providing more defensible environmental standards and regulations.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".