Mite non-reproduction, recapping behavior, and hygienic behavior (freeze-kill method) linked to <i>Varroa destructor</i> infestation levels in selected <i>Apis mellifera</i> colonies
Bibliographic record
Abstract
parasite and thus reducing its dependence on acaricides. However, the links between these behavioral traits are not yet well defined, which limits genetic progress in breeding programs. We measured the following behavioral varroa resistance traits: freeze-kill brood (FKB) and pin-kill brood (PKB) assays, varroa-sensitive hygiene (VSH), pupae removal, mite non-reproduction (MNR), and recapping activity. We found 2 negative and significant relationships: 1) between the recapping of cells infested with varroa and the total number of recapped cells, and 2) between the recapping of cells infested with varroa and VSH. We also selected the best predictive model of varroa infestation levels using the "step-wise" approach based on the Akaike information criterion. Our model revealed that MNR and FKB were significantly related to the varroa population levels, with a negative relationship; recapping was significantly related to mite infestation levels, with a positive relationship. Thus, a higher MNR or FKB score was linked to lower levels of mite infestation in colonies on August 14 (prior to fall infestation treatments); a higher recapping activity was linked to a higher level of mite infestation. Recapping behavior could be a useful trait to aid the selection of varroa-resistant bee lineages.
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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) | 0.000 | 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".