Wild Edible Fruits as a Source of Food and Medicine: A Study among Tribal Communities of Southern Khyber Pakhtunkhwa
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".