Probiotics and in-hive fermentation as a source of beneficial microbes to support the gut microbial health of honey bees
Bibliographic record
Abstract
Abstract Managed populations of honey bees (Apis mellifera Linnaeus; Hymenoptera: Apidae) are regularly exposed to infectious diseases. Good hive management including the occasional application of antibiotics can help mitigate infectious outbreaks, but new beekeeping tools and techniques that bolster immunity and help control disease transmission are welcome. In this review, we focus on the applications of beneficial microbes for disease management as well as to support hive health and sustainability within the apicultural industry. We draw attention to the latest advances in probiotic approaches as well as the integration of fermented foods (such as water kefir) with disease-fighting properties that might ultimately be delivered to hives as an alternative or partial antidote to antibiotics. There is substantial evidence from in vitro laboratory studies that suggest beneficial microbes could be an effective method for improving disease resistance in honey bees. However, colony level evidence is lacking and there is urgent need for further validation via controlled field trials experimentally designed to test defined microbial compositions against specific diseases of interest.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".