Larvicidal Activity of Leaf Extract From Mauritiella armata (Aceraceae) on Aedes aegypti and Culex quinquefasciatus (Culicidae)
Bibliographic record
Abstract
The mosquitoes Aedes aegypti and Culex quinquefasciatus (Diptera: Culicidae) are important vectors of several arboviruses, and are relevant public health problems. Conventional control, using chemical larvicides have selected resistant Culicidae populations and caused negative effects on the environment and human health. However, the use of plant extracts has represented a sustainable alternative for insect control. Popularly known as Xiriri, Mauritiella armata (Mart.) Burret (Aceraceae) is an abundant palm tree in Vereda ecosystems and has economic and social importance. In this study, the aim was to evaluate the larvicidal activity of the aqueous extract (AE) leaves of this plant on Ae. aegypti and Cx. quinquefasciatus larvae. The mortalities of larvae were analyzed after treatment with four concentrations of the extract, comparing with a negative control using mineral water. The AE promoted 100% efficacy against Ae. aegypti larvae at 7.9 mg/mL. The lethal concentration to promote 90% mortality of Cx. quinquefasciatus larvae was estimated at 30.57 mg/mL. After chromatographic analyses, flavonoids, catechin and carbohydrates were detected. AE from M. armata leaves presented high larvicidal activity against Ae. aegypti and Cx. quinquefasciatus, and represents a promising alternative to be used in vector control.
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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".