Detection of pathogen spillover between managed honey bees (Apis mellifera L.) and native pollinators (Bombus spp.) through quantification of RNA viruses
Bibliographic record
Abstract
RNA viruses are a contributor to recent honey bee disappearances and may have spilled over to bumble bees from honey bees (HB). This hypothesis is addressed by comparing the prevalence and intensity of seven viruses in bumble bees captured in proximity to and isolation from managed honey bee colonies. Additionally, sampling method for bees and different storage variables are compared for accuracy in estimating viruses in field-caught specimens. Prevalence was lower in isolated bumble bees for DWV, BQCV and SBV and higher for IAPV. KBV, ABPV and CBPV infections were rare. Virus intensity was higher in HB-exposed sites than unexposed sites for one virus and never higher in bumble bees than in honey bees. This suggests that spillover is likely, but viral dynamics are complicated and movement may occur in both directions. Additionally, specimens should be stored at -80⁰C with no medium recommended for relative preservation of host and viral RNA.
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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".