Pesticide Exposure and Effects on Non-<i>Apis</i> Bees
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bees are essential pollinators of many crops and wild plants, and pesticide exposure is one of the key environmental stressors affecting their health in anthropogenically modified landscapes. Until recently, almost all information on routes and impacts of pesticide exposure came from honey bees, at least partially because they were the only model species required for environmental risk assessments (ERAs) for insect pollinators. Recently, there has been a surge in research activity focusing on pesticide exposure and effects for non- Apis bees, including other social bees (bumble bees and stingless bees) and solitary bees. These taxa vary substantially from honey bees and one another in several important ecological traits, including spatial and temporal activity patterns, foraging and nesting requirements, and degree of sociality. In this article, we review the current evidence base about pesticide exposure pathways and the consequences of exposure for non- Apis bees. We find that the insights into non- Apis bee pesticide exposure and resulting impacts across biological organizations, landscapes, mixtures, and multiple stressors are still in their infancy. The good news is that there are many promising approaches that could be used to advance our understanding, with priority given to informing exposure pathways, extrapolating effects, and determining how well our current insights (limited to very few species and mostly neonicotinoid insecticides under unrealistic conditions) can be generalized to the diversity of species and lifestyles in the global bee community. We conclude that future research to expand our knowledge would also be beneficial for ERAs and wider policy decisions concerning pollinator conservation and pesticide regulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it