Selective Breeding for Low and High Varroa destructor Growth in Honey Bee (Apis mellifera) Colonies: Initial Results of Two Generations
Bibliographic record
Abstract
After two years of bidirectional selection for low and high rates of Varroa destructor population growth (LVG and HVG, respectively) in honey bee (Apis mellifera) colonies in Ontario, Canada, significant differences between the two genotypes were observed. LVG colonies had V. destructor population increases over the summer of 1.7 fold compared to 9.6 fold for HVG colonies by Generation 2. Additionally, HVG colonies had significantly higher mite infestation rates in adult bees compared to LVG colonies for both selected generations. DWV prevalence and levels were significantly higher in HVG colonies than in LVG colonies in Generation 1 but not in Generation 2. Winter mortality rates of Generation 1 colonies were significantly different at 26% and 14% for the HVG and LVG genotypes, respectively. The results of this study thus far indicate that selection for LVG may result in colonies with lower V. destructor infestation rates, lower prevalence, and levels of DWV and higher colony winter survivorship. Future work will focus on determining what mechanisms are responsible for the genotypic differences, estimating genetic parameters, and molecular analyses of the genotypes to identify candidate genes associated with resistance to V. destructor and DWV that could potentially be used for marker-assisted selection.
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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 | 0.001 | 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 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".