Repellent effectiveness of seven plant essential oils, sunflower oil and natural insecticides against horn flies on pastured dairy cows and heifers
Bibliographic record
Abstract
Plant essential oils (basil, geranium, balsam fir, lavender, lemongrass, peppermint, pine and tea tree), mixed with either sunflower oil or ethyl alcohol, were applied at 5% concentrations to the sides of Holstein cattle. Pastured cattle treated with essential oils diluted in sunflower oil had less flies than the untreated control for a 24-h period. However, the essential oil treatments were not significantly different than the carrier oil alone. Barn-held heifers treated with essential oils and sunflower oil alone had significantly less flies than the untreated control for up to 8 h after treatment. Basil, geranium, lavender, lemongrass and peppermint repelled more flies than sunflower oil alone for a period ranging from 1.5 to 4 h after treatments applied to heifers. All essential oils repelled > 75% of the flies on the treated area for 6 and 8 h on pastured cows and indoor heifers, respectively. Geranium, lemongrass and peppermint stayed effective for a longer duration. Essential oils mixed with ethyl alcohol demonstrated less repellence than when mixed with the carrier oil. Safer's soap, natural pyrethrins without piperonyl butoxide and ethyl alcohol alone were not efficient at repelling flies. Essential oils could be formulated for use as fly repellents in livestock production.
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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".