Drone exposure to the systemic insecticide Fipronil indirectly impairs queen reproductive potential
Bibliographic record
Abstract
A species that requires sexual reproduction but cannot reproduce is doomed to extinction. The important increasing loss of species emphasizes the ecological significance of elucidating the effects of environmental stressors, such as pesticides, on reproduction. Despite its special reproductive behavior, the honey bee was selected as a relevant and integrative environmental model because of its constant and diverse exposure to many stressors due to foraging activity. The widely used insecticide Fipronil, the use of which is controversial because of its adverse effects on honey bees, was chosen to expose captive drones in hives via syrup contaminated at 0.1 μg/L and gathered by foragers. Such environmental exposure led to decreased spermatozoa concentration and sperm viability coupled with an increased sperm metabolic rate, resulting in drone fertility impairment. Subsequently, unexposed queens inseminated with such sperm exhibited fewer spermatozoa with lower viability in their spermatheca, leaving no doubt about the detrimental consequences for the reproductive potential of queens, which are key for colony sustainability. These findings suggest that pesticides could contribute to declining honey bee populations through fertility impairment, as exemplified by Fipronil. More broadly, reproductive disorders should be taken into consideration when investigating the decline of other species.
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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".