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Record W2920880102

Moringa for nutritional security (Moringa oleifera Lam.)

2019· article· en· W2920880102 on OpenAlexvenueno aff
Sandeep Gunalan

Bibliographic record

VenueInternational Journal of Horticulture · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMoringa oleifera research and applications
Canadian institutionsnot available
Fundersnot available
KeywordsMoringaPolyphenolTraditional medicineFodderCropBiotechnologyBiologyFood scienceAntioxidantAgronomyMedicine
DOInot available

Abstract

fetched live from OpenAlex

Moringa oleifera Lam. Lam. A crop of the tropical and subtropical countries is grown universally as food, fodder and medicine. The seed, leaves, roots and flowers are often used in traditional medicine products in human food. Some studies using powdered leaf preparation of Moringa oleifera have demonstrated anti-dyslipidaemic and antidiabetic activities. Different studies have shown that hydro alcohol and aqueous activities such as antihypertensive, antiulcer, antioxidant, immunomodulation and analgesic actions. Phenolic, polyphenols, alkaloids and flavonoids are responsible for the observed effect commercial production, processing and utilization of this wonder crop is gaining momentum in many of the countries due to its multifaceted uses. The aim of this research paper was to review Moringa oleifera as a potential tree for its wide range of uses. Therefore, there is an urge to focus research on the uses of moringa leaves that can be value added. This research work would serve as a background for future research programmes in post-harvest management and value addition in moringa.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.012
GPT teacher head0.266
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations27
Published2019
Admission routes1
Has abstractyes

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