Evaluating Efficacy of Fumagilin-B <sup>®</sup> Against Nosemosis and Tracking Seasonal Trends of <i>Nosema</i> spp. in Nova Scotia Honey Bee Colonies
Bibliographic record
Abstract
Abstract The efficacy of the antimicrobial Fumagilin-B ® against nosemosis was evaluated in both spring and autumn feeding treatments following label directions in seventy-two honey bee ( Apis mellifera ) colonies across three apiaries in Nova Scotia, Canada. The seasonal trend of Nosema spp. spore loads was also tracked in these same colonies throughout a thirteen-month period (February 2018 – March 2019). We found the spring Fumagilin-B ® treatment to be effective at significantly suppressing Nosema spp. spore levels below the recommended treatment threshold. There was no effect of Fumagilin-B ® treatment in the autumn based on low spore levels at this time. We detected a drastic increase in Nosema spp. spore loads as May progressed but a decline in spores in summer (June–September). By October, there was another increase in spore levels, but this increase did not exceed the economic treatment threshold. Across seventeen collection periods in both control and Fumagilin-B ® colonies, 74% (25) of samples tested positive for Nosema ceranae , while 26% (9) contained no Nosema spp. spores. No Nosema apis spores were detected during this trial. Our results indicate that Fumagilin-B ® is an effective management practice in the spring, but colonies should still be monitored in the autumn. Our data also support that the Nosema species profile is shifting to be exclusively N. ceranae and the treatment threshold for Fumagilin-B ® may need to be updated to reflect this, as the threshold was originally developed for N. apis .
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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".