A non-invasive method for profiling the gut microbiome and virome of honey bee queens
Bibliographic record
Abstract
ABSTRACT High honey bee colony mortality worldwide has underscored the critical role of queen bee health in colony survival, with poor queen quality frequently linked to colony losses. The gut microbiome plays fundamental roles in immunity, nutrition, and reproduction, making its characterization essential for understanding stressors that impact queen health, longevity, and fecundity, yet its role in mediating stress responses remains poorly understood. Here, we present a novel, non-invasive method for collecting feces from queen honey bees and demonstrate its potential as a powerful tool for profiling the gut microbiome, detecting stressor exposure, and screening for viral infections. This approach permits repeated, longitudinal assessments of individual queens, providing unprecedented insights into how environmental and pathogenic pressures influence queen health, longevity, and reproductive capacity. Beyond research applications, benefits include evaluating queens before colony introduction and mitigating disease transmission risks in international trade, where pathogen spread remains a major regulatory challenge.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".