MétaCan
Menu
Back to cohort
Record W2947940631 · doi:10.5539/jas.v11n8p129

Larvicidal Activity in vivo of Ethanolic and Aqueous Extracts From Moringa (Moringa oleifera Lam.) on Aedes aegypti L. (Diptera: Culicidae)

2019· article· en· W2947940631 on OpenAlexvenueno aff
Antônio Carlos Alves, Toshik Iarley da Silva, Francisco Roberto de Azevedo, Estelita Pereira Lima, Renata Rocha Virgulino, Clarice E. T. L. Costa, José Valmir Feitosa

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMoringa oleifera research and applications
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMoringaAedes aegyptiDistilled waterTraditional medicineLarvaBiologyAedesAqueous extractChemistryBotanyMedicineChromatography

Abstract

fetched live from OpenAlex

The insecticides properties of Moringa oleifera (moringa) were evaluated in Aedes aegypti larvae, throughout an entirely randomized trial, represented by ethanolic and aqueous extracts obtained from leaves, flowers, barks, seeds and moringa roots, besides the control group. Five batches of 10 in 3rd stadium larvae were distributed in distilled water, added an extract concentration of 50 mL L-1, and the control treatment (distilled water). The test reading was measured after 24, 48 and 72 hours after larvae exposure, were considered dead those who did not respond to a mechanical stimulation of a clamp. The seed ethanolic extract produced the best performance after 24 hours (34% mortality), but after 48 hours, the flower extract was the more potent (38% mortality). The largest larvicidal activity was observed with the extract concentration of 90 mL L-1. The results indicate that moringa has larvacides properties against Aedes, but its chemical constituents need to be isolated and tested separately to enhance your larvicidal activity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.251
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural ScienceSame topicMoringa oleifera research and applicationsFrench-language works237,207