Pesticide Risk Assessment in a Changing World
Bibliographic record
Abstract
Pesticide risk assessments currently rely on surrogate species and focus primarily on acute lethality metrics, failing to capture the broader impacts on non-target organisms and thus biodiversity. Under the directives of regulatory agencies worldwide, this traditional approach overlooks the complex interactions between multiple stressors, including climate change, land-use shifts, and pesticide transformation products. Pesticide risk assessments must therefore undergo a paradigm shift to account for these complex interactions, which disproportionately affect insect pollinators, other non-target species, and biodiversity at large. While prior work has highlighted the need to move beyond single-species models, emerging evidence on nonlinear stressor interactions and the ecological consequences of transformation products highlight critical gaps in current frameworks. Here, we synthesize insights from recent research to propose a holistic approach for environmental risk assessments that integrates ecological and evolutionary complexities in the context of global change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".