Probiotics and in-hive fermentation as a source of beneficial microbes to support the gut microbial health of honey bees
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it