Effects of different essential oils on the acceptability and palatability of cereal-based baits for laboratory mice
Bibliographic record
Abstract
The aim of our study was to investigate the possibility of application of essential oils as additives in cereal-based rodenticide baits, at rates which prevent mould development and are applicable in humane medicine. Also, the purpose of these oils would be to extend the utility value of baits and reduce the use of antifungal ingredients that usually affect bait acceptance. The effects of essential oils of ten different plant species, applied at rates of one and two ml per kg of plain bait, on bait acceptance and palatability in choice feeding tests for Swiss mice were studied under controlled laboratory conditions. Baits were prepared according to relevant EPPO standards (2004). The effects of essential oils on bait acceptance and palatability for Swiss mice were determined in choice feeding tests using a formula by Johnson and Prescott (1994). There was no significant statistical difference in effects between groups and sexes regarding weight change in the experiment. During the experiment and recovery period, neither change in mice behavior no deaths were observed. Cinnamon and anise essential oils, commonly used as attractants, and clove oil, were most effective because they had no negative effect on bait acceptance and palatability, while fenchel and bergamot oils showed repellent activity that grew as the oil content in baits increased.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".