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Acaciella angustissima (Mill.) Brit. & Rose: Botanical Features, Distribution, Medicinal and Pharmacological Properties

2020· article· en· W3206599155 on OpenAlexvenueno aff
Collen Musara, Elizabeth Bosede Aladejana

Bibliographic record

VenueJournal of Pharmacy and Nutrition Sciences · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed and Plant Biochemistry
Canadian institutionsnot available
Fundersnot available
KeywordsShrubTraditional medicineAgroforestryBiologyMedicineBotany

Abstract

fetched live from OpenAlex

Acaciella angustissima (Mill.) Brit. & Rose is a multipurpose deciduous thornless shrub or small tree that belongs to the family Fabaceae and subfamily Mimosoideae. This study aimed to explore A. angustissima, a leguminous shrub with medicinal, ecological and industrial potential. A mixed-method approach, which included consolidating quantitative and qualitative research, was utilized to put together the review with the main focus being on sub-Saharan Africa. However, case studies and literature from South Africa were also utilized. A. angustissima is a good source of phenolic compounds. It is used to relieve painful toothache, rheumatism, skin lesions, bloody diarrhea and mucoid diarrhea. It also displays a mild antimicrobial effect and has the ability to inhibit growth in malignant tumors. The study acknowledged Acaciella angustissima as an important agroforestry tree species that improve the quality of life of resource-poor farmers, reducing poverty and promoting sustainability of the natural resources base and economic growth

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.073
GPT teacher head0.295
Teacher spread0.223 · 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 designObservational
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

Citations3
Published2020
Admission routes1
Has abstractyes

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