Bumblebee colony development following chronic exposure to field-realistic levels of the neonicotinoid pesticide thiamethoxam under laboratory conditions
Bibliographic record
Abstract
Neonicotinoid pesticides are used in agriculture to reduce damage from crop pests. However, beneficial insects such as bees can come into contact with these pesticides when foraging in treated areas, with potential consequences for bee declines and pollination service delivery. Honeybees are typically used as a model organism to investigate insecticide impacts on bees, but relatively little is known about impacts on other taxa such as bumblebees. In this experiment, we chronically exposed whole mature bumblebee (Bombus terrestris) colonies to field-realistic levels of the neonicotinoid thiamethoxam (2.4ppb & 10ppb) over four weeks, and compared colony growth under laboratory conditions. We found no impact of insecticide exposure on colony weight gain, or the number or mass of sexuals produced, although colonies exposed to 2.4ppb produced larger males. As previous studies have reported pesticide effects on bumblebee colony growth, this may suggest that impacts on bumblebee colonies are more pronounced for colonies at an earlier stage in the reproductive cycle. Alternatively, it may also indicate that thiamethoxam differs in toxicity compared to previously tested neonicotinoids in terms of reproductive effects. In either case, assessing bumblebee colony development under field conditions is likely more informative for real world scenarios than tests conducted in laboratory conditions.
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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 | 0.000 | 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) | 0.001 | 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".