Honey bee health and productivity in Ontario, Canada: a multifactorial epidemiological approach
Bibliographic record
Abstract
Beekeepers in Ontario, Canada experienced record-breaking winter losses in 2014, and an unexplained rise in honey bee mortality incident reports between 2012-2014. The purpose of this thesis was to improve the understanding of the beekeeping industry in Ontario, and identify disease, management, and spatial factors associated with in-season (i.e., non-winter) colony mortality. This retrospective cross-sectional study utilized data (2014) from an Ontario-wide survey of beekeepers, and spatial corn crop locations obtained from Agriculture and Agrifood Canada. Results of descriptive analyses showed that Varroa mites and queen supersedure were among the key self-reported challenges facing many responding beekeepers. Stratified results between small scale beekeepers (<50 colonies) versus commercial beekeepers (≥50 colonies), showed commercial beekeepers have significantly more years of experience and higher honey production. Total in-season colony loss (i.e., cumulative incidence of colony mortality) for the study population was 19.6% (978 dead colonies and 4,992 colonies at risk; 95% confidence interval (CI)=18.5-20.7%). In-season colony loss was significantly lower for beekeepers with >3 years of experience (versus ≤2 years; odds ratio (OR)=0.40; 95% CI=0.18-0.90), and those managing ≥2 yards (versus 1 yard; OR=0.37; 95% CI=0.17-0.80). Knowledge of Varroa presence significantly lowered the odds of in-season colony loss (OR=0.34; 95% CI=0.16-0.76). Additionally, colonies with queens that were 1-year old (OR=0.34; 95% CI=0.14-0.83) or ≥2 years old (OR=0.15; 95% CI=0.05-0.45) were associated with decreased odds of in-season colony loss compared to queens <1 year of age. In spatial analyses, the presence of corn (as a surrogate for neonicotinoid exposure) in the same 10 km2 quadrat of a yard was not significantly associated with in-season colony mortality. In summary, this thesis finds in-season colony loss to have significant associations with beekeeper and disease factors but does not support the role of corn exposure. This provides opportunities to actively support and improve colony health by focusing on increased beekeeper education, particularly for small-scale beekeepers, in the areas of disease management and queen health. Future research that includes surveillance of in-season loss over multiple years, for both small-scale and commercial beekeepers, would likely contribute to a better understanding of honey bee health in Ontario.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".