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Record W3039760044 · doi:10.36713/epra4698

Importance of Prosopis cineraria (L.) Druce as a Medicinal Plant: A Review

2020· review· en· W3039760044 on OpenAlexaff
Rancey Jain, Swati Jayswal, Bharat Maitreya

Bibliographic record

VenueEPRA International Journal of Multidisciplinary Research (IJMR) · 2020
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPolysaccharides Composition and Applications
Canadian institutionsImpact
Fundersnot available
KeywordsProsopisBiologyMedicinal plantsPhytochemicalTraditional medicineBotanyMedicine

Abstract

fetched live from OpenAlex

Prosopis cineraria (L.) Druce (Family Leguminosae, Sub-family Mimosoideae) is known as “Khejri tree”. It is a deep rooted, nitrogen fixing, multipurpose endemic tree to the hot deserts of India. It has been used since ancient times, particularly for medicinal purposes, traditionally and socially. Traditionally paste, gum, smoke from leaves and pods are applied for antidiabetic, anticancer, anti-inflammatory and antimicrobial purposes. Phytochemical component of Prosopis such as flavonoids, tannins, alkaloids, quinones or phenolic compounds demonstrate various biofunctions, such as analgestic, anthelmintic, antibiotic, antimetic, microbial antioxidant, antimalarial, anti-protozoal, antipustule and antiulcer activities. Dry pods of Prosopis cineraria are also known as sangria pods and it is the main part of Rajasthani dishes and also have a broader range of pharmaceutical applications like in pain, high cholesterol level, diabetes, an anemia, kidney and liver disorders. The Prosopis cineraria plays an important role in the socio-economic development of the farmers. The review explores the use of Prosopis cineraria across the all disciplines for its medicinal value and deals with cultivation, nutrition, commercial and prominent pharmacological properties of the “wonder tree”. KEYWORDS: - Prosopis cineraria, antimicrobial activity, phytochemicals, khejri tree, sangri pods, pharmacological properties.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.164
GPT teacher head0.455
Teacher spread0.291 · 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 designOther design
Domainnot available
GenreReview

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

Citations3
Published2020
Admission routes1
Has abstractyes

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Same venueEPRA International Journal of Multidisciplinary Research (IJMR)Same topicPolysaccharides Composition and ApplicationsFrench-language works237,207