Comparative Efficiency of Four Repellents against <i>Anopheles gambiae s.s.</i>
Bibliographic record
Abstract
Personal protection tools are of paramount importance in vector borne disease control. The use of repellents is an additional tool for use when out of bed. Four repellents N, N-Diethyl-3-methylbenzamide (DEET), Maskitta, lemon grass and Menthol propylene glycol carbonate (MR08) were examined in this experiment for their protective efficiency against malaria vectors Anopheles gambiae senso stricta. The four repellents DEET, Maskitta, Citronella and MR08 were formulated on volume based ratio against glycerin oil. The repellent dosages were evaluated against Anopheles gambiae in a 30×30×30 cm cage. Treated arm of a volunteer was exposed in the cage for a period of one hour for each dosage. Likewise for negative control. Experiments were conducted in a room with temperature and humidity of 27±2 degree Celsius and 75±2% respectively. Each repellent experiment had six replicates. In this study the results showed that, the protective efficiency was highest in Maskitta, followed by DEET, MR08 and lemon grass was least protective in all dosages. DEET was the golden standard positive control and glycerin was negative control in these experiments. Maskitta reached a protection efficiency of 100% similar to DEET while MRO8 were above 80% in all concentrations. From the results of these experiments, the use of repellents for personal protective gear has a significant role in reducing human-vector contact. Maskitta which is a newly formulated repellent showed a protection efficiency of 100%, this is evidence that more scientific research should be carried out in order to have more quality effective products for personal protection against mosquitoes.
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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".