Honey bee survival is affected by interactions between field-relevant rates of fungicides and insecticides used in apple and blueberry production
Bibliographic record
Abstract
Certain compounds of low toxicity can increase the susceptibility of an organism to toxic substances; this is known as potentiation. Demethylation inhibiting (DMI) fungicides can potentiate insecticides by impairing the production of detoxification enzymes. As both DMI fungicides and insecticides can be used near or during crop bloom, the combination may be hazardous if exposed to pollinators. Using pesticides used in blueberry or apple production, we conducted laboratory bioassays to test how combinations of field-relevant concentrations of DMI fungicides and insecticides affected honey bee ( Apis mellifera) survival. We found propiconazole, a DMI fungicide, potentiated the toxicity of the neonicotinoid insecticide acetamiprid. We found no evidence of propiconazole potentiating field-relevant concentrations of the spinosyn insecticide spinetoram. We also found that the DMI fungicide flusilazole potentiated spinetoram but not acetamiprid. A fungicidal formulation combining pyraclostrobin and boscalid did not potentiate either insecticide. Given that bees can be simultaneously exposed to multiple pesticides, understanding the potential of pesticide potentiation and synergism may help mitigate risks associated with pollinator exposure to pesticides.
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.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".