Lemongrass and Cinnamon Bark: Plant Essential Oil Blend as a Spatial Repellent for Mosquitoes in a Field Setting
Bibliographic record
Abstract
Plant essential oils (EOs) have been considered as spatial repellents to help disrupt the pathogen transmission cycle of mosquitoes. Our objective was to assess spatial repellency effects of EOs on the tropical yellow fever mosquito, Aedes aegypti (L.) (Diptera: Culicidae) and on local mosquito populations in coastal British Columbia (Canada). In laboratory experiments using protocols of the World Health Organization, three of the solitary EOs tested proved repellent to Ae. aegypti: cinnamon bark, lemongrass, and rosemary. Binary combinations of select EOs enhanced the repellent effect of single EOs through synergistic interactions. The EO blend of geranium and peppermint lowered the RD50 (the dose required to obtain 50% repellency) of each solitary EO by >1,000-fold. Compared with binary EO blends, ternary EO blends were typically less repellent to mosquitoes, possibly due to a dilution effect of the most effective EO constituent(s) in the blend. In field experiments, the EO blend of lemongrass and cinnamon bark expressed spatial repellency towards the cool weather mosquito, Culiseta incidens (Thomson) (Diptera: Culicidae), even when this blend was disseminated from devices as much as 1 m away from a sentinel trap releasing attractive vertebrate host odorants and CO2. Deployment of EOs as spatial repellents in small outdoor gatherings could help protect humans from mosquito-borne diseases, particularly when this tactic is coupled with other tools of mosquito management.
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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".