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Record W4390061997 · doi:10.3390/plants13010039

Wild Edible Fruits as a Source of Food and Medicine: A Study among Tribal Communities of Southern Khyber Pakhtunkhwa

2023· article· en· W4390061997 on OpenAlexaff
Sheikh Zain Ul Abidin, Raees Khan, Mushtaq Ahmad, Alain Cuerrier, Muhammad Zafar, Asad Ullah, Jabbar Khan, Asma Saeed, Wahidah H. Al‐Qahtani, Mohsin Kazi

Bibliographic record

VenuePlants · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEthnobotanical and Medicinal Plants Studies
Canadian institutionsUniversité de Montréal
FundersKing Saud University
KeywordsKhyber pakhtunkhwaGeographySocioeconomicsTraditional medicineBiologyMedicine

Abstract

fetched live from OpenAlex

Botanical surveys in all parts of Pakistan are mainly focused on ethnomedicinal uses of plants, and very little attention has been paid to documenting edible wild fruit species (EWFs). Multiple methodologies and tools were used for data collection. In a recent survey 74 EWF species belonging to 29 families were documented, including their medicinal uses for the treatment of various diseases. The most cited (23%) preparation method was raw, fresh parts. The UV and RFC of EWF species ranged from 0.08 to 0.4 and from 0.02 to 0.18, respectively. In terms of specific disease treatments and their consensus, the ICF ranged from 0 to 0.38. Sexual, gastrointestinal, and respiratory disorders had the highest use reports, and 11 species of plants had the highest FL of 100%. On the basis of uses reported by the inhabitants of seven districts of Southern Khyber Pakhtunkhwa Province, the CSI ranged from the lowest 1.3 to the highest 41. It is concluded that the traditional uses of EWF species depend mainly on socio-economic factors rather than climatic conditions or the number of species. However, there is a gradual loss of traditional knowledge among the younger generations. The present survey is the first baseline study about the socio-economic dimension of local communities regarding the use of EWF species for food as well as medicine.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.055
GPT teacher head0.266
Teacher spread0.211 · 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 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

Citations8
Published2023
Admission routes1
Has abstractyes

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