Understanding and comparing relative pesticide risk among North American wild bees from their association with agriculture
Bibliographic record
Abstract
In North America, approximately 21 % (739 species) of the total wild bee diversity is known to be associated with crops, with bee species varying in the extent of this association. While current evaluations of pesticide effects on bees primarily focus on a limited subset of species, a new focus is needed to ensure comprehensive protection of all wild bees in agricultural contexts. This study introduces a novel approach to characterize and compare the relative potential pesticide risk for wild bee species of their association with crops. Using intrinsic bee vulnerability traits and extrinsic factors like crop toxic loads and association strength, we calculated Bee-Crop Risk Scores for 594 wild bee species, identifying those experiencing the highest potential risk from pesticide exposure in North American agroecosystems. We discuss the influence of intrinsic and extrinsic factors on the relative potential risk calculated and outline avenues for refining our approach. As most species facing the highest potential risk from pesticide exposure across North America are ground-nesters, our study suggests that species (e.g., Osmia spp., Megachile spp.) commonly proposed as models for pesticide risk assessments may not accurately represent risk for those bee species facing the highest potential risk in agricultural contexts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".