Requeening queenright honey bee colonies with queen cells in honey supers
Bibliographic record
Abstract
Many Canadian beekeepers replace a subset of their honey bee queens annually. However, introducing a new queen to a honey bee colony is a management practice with a high degree of uncertainty. Despite the consensus that it is most effective to introduce queens to queenless colonies, some commercial beekeepers claim success with introducing queen cells into the honey super of queenright colonies. We tested the success rate of this practice by introducing queen cells to 100 queenright colonies in southern Alberta during a honey flow. The genotypes of the resultant offspring drones were determined using the microsatellite marker A76 to identify their laying queen mothers. Our results show that new queens successfully supersede original queens in 6% of queenright colonies, suggesting that the practice does not result in the new queen taking over leadership in most colonies. Additionally, supersedure by daughter queens is more common (13%) than new queen supersedure when introducing queen cells to queenright colonies during a honey flow. However, there could be a benefit to the practice of requeening queenright colonies with queen cells in honey supers if the colonies that accepted a new queen (whether a daughter of or unrelated to the old queen) were colonies with a failing queen.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".