Efficacy and Safety of Essential Oils in The Control of Mosquito: A Review of Research Findings
Bibliographic record
Abstract
For millennia, people have utilized essential oil-rich plants to control mosquitoes and other hematophagous insects. A review of the literature found that terpenoids and "sesquiterpenoid-rich oils" were effective in mosquito control. Due to the benign impression and successful prevention of mosquito bites, there has been a recent surge in the acceptance of biobased agents as mosquito control solutions, in conjunction with the worldwide demand to take action to battle climate change and its consequences. Materials for this review, which included works published for the last decade and even earlier, were sourced from the research databases Scopus, Web of Science, PubMed, ERIC, IEEE Xplore, ScienceDirect, Directory of Open Access Journals (DOAJ), and JSTOR using the keywords "essential oils," "larvicidal activity," "oviposition deterrent," "repellents," "toxicity," "safety," and "efficacy." " Recent research has found that low and middle-income African populations prefer plant-based repellents over manufactured chemical repellents such as N, N-diethyl-m-toluamide and N, N-diethyl phenylacetamide. Although ethnobotanical studies have demonstrated that biobased repellents are effective, environmentally friendly, and have almost no biohazard impact, they are also a source of bioactive substances for the creation of novel mosquito repellent products. The World Health Organization and other relevant agencies have yet to certify and accept the bulk of these plants with potential viability. Furthermore, there is a very limited comparison list of the efficiency and safety of these plant-based repellents. As a result, there is a need to further investigate these bio-based natural repellents and their formulations for successful mosquito control, allowing for the production of novel repellents that deliver high repellence while also ensuring consumer safety.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".