The Potential for Using Ozone to Decrease Pesticide Residues in Honey Bee Comb
Bibliographic record
Abstract
Ozone is a strong oxidizer, and we evaluated its potential to eliminate pesticides from honeycomb and empty honey bee hives. Honey bees are exposed to pesticides when foraging for nectar and pollen and when beekeepers use in-hive chemical pest control measures. Persistent pesticides can accumulate in the hive over years, potentially harming the bees. Honeycomb is removed from bee colonies for honey extraction and then placed back on the colonies at a later date, providing a time when combs could be fumigated to eliminate or reduce pesticide residues. We found that ozone gas at a rate >920 mg O3/m3 for 10-20 h lowers coumaphos residues on a glass surface by 93-100% and tau-fluvalinate by 75-98%. Ozone was less effective at eliminating pesticides on beeswax, and residues were more effectively eliminated with new combs (comb built by bees within 3 y) than with old combs (combs used by beekeepers for >10 y). Ozone significantly reduced dimethylphenyl formamide, chlorpyrifos, and fenpyroximate contaminations in comb. When comb is treated with ozone, an off-odor is created, but the volatiles were found to be primarily straight chain aldehydes and carboxylic acids that are probably harmless to bees and humans. Ozone may have some utility for lowering pesticide residues in bee hives, but it would be more effective if a mechanism could be found that provides better penetration into wax, a goal not fully accomplished in our method.
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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) | 0.001 | 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".