Comparing the efficacy of synthetic Varroacides and <i>Varroa destructor</i> phenotypic resistance using Apiarium and Mason jar bioassay techniques
Bibliographic record
Abstract
BACKGROUND: Varroa mite, Varroa destructor, is a major threat for honey bee, Apis mellifera, colonies. Beekeepers have used synthetic Varroacides against Varroa mite for decades, but resistance to organophosphates, pyrethroids and formamidine has been reported in many locations worldwide. The goals of this study were to develop a reliable bioassay to assess efficacy and phenotypic resistance to commercial Varroacides. In this study, efficacy and Varroa resistance was evaluated using the Apiarium technique in comparison to the Mason jar method. RESULTS: Among tested Varroacides, a high efficacy (89%) for Apivar was identified when compared to Bayvarol (58%), Apistan (44%) and CheckMite (6%), in a 24 h assessment. We also found that CheckMite was toxic to bees in the Mason jar method. In addition, the Apiarium technique revealed a case of phenotypic resistance to Bayvarol, Apistan and CheckMite in the mite population evaluated. CONCLUSION: A laboratory protocol was developed using the Apiarium method to evaluate Apivar efficacy. Collectively, the findings indicated that the Apiarium methodology provided a reliable technique to measure Varroacide efficacy and determine the presence of phenotypic resistance in V. destructor. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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 | 0.000 | 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) | 0.001 | 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".