Nosema ceranae Infections in Honey Bees (Apis mellifera) Treated with Pre/Probiotics and Impacts on Colonies in the Field
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
Alternatives to the antibiotic fumagillin for the control of Nosema ceranae, a gut parasite of the honey bee, are needed. The prebiotics eugenol, chitosan, and naringenin and the probiotic Protexin® (Enterococcus faecium) provided in sugar syrup or protein patty either in spring or fall were evaluated for their effects on N. ceranae infection, colony population, honey yield and winter survivorship using field colonies. In the first year, spring treatments with eugenol, naringenin, and Protexin® significantly reduced N. ceranae infection and increased honey production, while Protexin® also increased adult bee populations and chitosan was ineffective. Fall treatments increased survivorship and decreased N. ceranae infection the following spring. In the second year, selected compounds were further tested with a larger number of colonies per treatment and only protein patty used in the spring and sugar syrup in the fall. Protexin® and naringenin significantly decreased N. ceranae infections and increased the population of adult bees after spring treatment, but did not affect honey yields. There were no differences between treatments for colony winter mortality, but surviving colonies that had been treated with Protexin® and naringenin were significantly more populated and had lower N. ceranae spore counts than control, non-treated colonies. Protexin® and naringenin were the most promising candidates for controlling N. ceranae and promoting honey bee populations, warranting further investigation. Future research should investigate the optimal colony dose and treatment frequency to maximize colony health.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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