Risk assessments underestimate threat of pesticides to wild bees
Bibliographic record
Abstract
Abstract Ecological risk assessments (ERAs) are crucial when developing national strategies to manage adverse effects from pesticide exposure to natural populations. Yet, estimating risk with surrogate species in controlled laboratory studies jeopardizes the ERA process because natural populations exhibit intraspecific variation within and across species. Here, we investigate the extent to which the ERA process underestimates the risk from pesticides on different species by conducting a meta‐analysis of all records in the ECOTOX Knowledgebase for honey bees and wild bees exposed to neonicotinoids. We found the knowledgebase is largely populated by acute lethality data on the Western honey bee and exhibits within and across species variation in LD50 up to 6 orders of magnitude from neonicotinoid exposure. We challenge the reliability of surrogate species as predictors when extrapolating pesticide toxicity data to wild pollinators and recommend solutions to address the (a)biotic interactions occurring in nature that make such extrapolations unreliable in the ERA process.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".