A Bio-Economic Case Study of Canadian Honey Bee (Hymenoptera: Apidae) Colonies: Marker-Assisted Selection (MAS) in Queen Breeding Affects Beekeeper Profits
Bibliographic record
Abstract
Over the past decade in North America and Europe, winter losses of honey bee (Hymenoptera: Apidae) colonies have increased dramatically. Scientific consensus attributes these losses to multifactorial causes including altered parasite and pathogen profiles, lack of proper nutrition due to agricultural monocultures, exposure to pesticides, management, and weather. One method to reduce colony loss and increase productivity is through selective breeding of queens to produce disease-, pathogen-, and mite-resistant stock. Historically, the only method for identifying desirable traits in honey bees to improve breeding was through observation of bee behavior. A team of Canadian scientists have recently identified markers in bee antennae that correspond to behavioral traits in bees and can be tested for in a laboratory. These scientists have demonstrated that this marker-assisted selection (MAS) can be used to produce hygienic, pathogen-resistant honey bee colonies. Based on this research, we present a beekeeping case study where a beekeeper's profit function is used to evaluate the economic impact of adopting colonies selected for hygienic behavior using MAS into an apiary. Our results show a net profit gain from an MAS colony of between 2% and 5% when Varroa mites are effectively treated. In the case of ineffective treatment, MAS generates a net profit benefit of between 9% and 96% depending on the Varroa load. When a Varroa mite population has developed some treatment resistance, we show that MAS colonies generate a net profit gain of between 8% and 112% depending on the Varroa load and degree of treatment resistance.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".