Pesticide-induced disturbances of bee gut microbiotas
Bibliographic record
Abstract
Social bee gut microbiotas play key roles in host health and performance. Worryingly, a growing body of literature shows that pesticide exposure can disturb these microbiotas. Most studies examine changes in taxonomic composition in Western honey bee (Apis mellifera) gut microbiotas caused by insecticide exposure. Core bee gut microbiota taxa shift in abundance after exposure but are rarely eliminated, with declines in Bifidobacteriales and Lactobacillus near melliventris abundance being the most common shifts. Pesticide concentration, exposure duration, season and concurrent stressors all influence whether and how bee gut microbiotas are disturbed. Also, the mechanism of disturbance-i.e. whether a pesticide directly affects microbial growth or indirectly affects the microbiota by altering host health-likely affects disturbance consistency. Despite growing interest in this topic, important questions remain unanswered. Specifically, metabolic shifts in bee gut microbiotas remain largely uninvestigated, as do effects of pesticide-disturbed gut microbiotas on bee host performance. Furthermore, few bee species have been studied other than A. mellifera, and few herbicides and fungicides have been examined. We call for these knowledge gaps to be addressed so that we may obtain a comprehensive picture of how pesticides alter bee gut microbiotas, and of the functional consequences of these changes.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".