Commercial probiotic formulas Bactocell and Levucell promote spring brood production in <i>Apis mellifera</i> L. (Hymenoptera: Apidae) colonies
Bibliographic record
Abstract
Honey bees are essential pollinators for several economically important crops. In temperate countries, honey bee colonies face multiple threats during the overwintering period, such as food availability, diseases, and confinement. Beekeepers commonly use chemicals to improve colony health during winter, but these products can have a negative impact on bee health and pathogens can develop resistance to them. Thus, there is a need for further development of alternative treatments. The aim of this study was to evaluate the impact of one endogenic bacterium (Bombella apis) and 2 commercial probiotic formulas (Bactocell and Levucell) on colony survival, spring development, and Vairimorpha (formerly Nosema) spp. spore count. Probiotic treatments were given in 1: 1 sugar syrup in October 2017 and April 2018, once a week for 2 wk. One experimental group was given Fumagilin-B, the only product approved in Canada to prevent nosemosis, once in October. The administration of 2 commercial probiotics, Bactocell (Pediococcus acidilactici) and Levucell (Saccharomyces cerevisiae boulardii), led to a significant increase in the number of sealed brood cells in spring. None of the probiotic treatments impacted the honey bee gut load of Vairimorpha spp. spores. The results suggest that beneficial microorganisms can improve spring development and performance of honey bee colonies.
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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".