Ethnomedical Practices and Medicinal Plant Use in Tanjung and Seponjen Villages, Jambi, Indonesia
Bibliographic record
Abstract
Communities living in and around forest areas have local wisdom regarding utilization of plants, particularly for medicinal purposes.They possess important knowledge about this, including the habitat, growth form, identification of plant species, parts used, methods of preparation, and their medicinal efficacy.This study was conducted from September to November 2021 and aims to analyze the diversity of medicinal plants and their utility values through field surveys and community interviews.This study was conducted in Tanjung Village and Seponjen Village, Kumpeh District, surrounding Orang Kayo Hitam (OKH) Forest Park, which is located on the border of Muaro Jambi Regency and East Tanjung Jabung, Jambi Province.The study found 66 medicinal plants from 42 families, among which turmeric has the highest utility value of 0.37.Several types of medicinal plants that are dominant come from the Zingiberaceae, Fabaceae, and Piperaceae families, with extensive benefits for treating common diseases such as digestive disorders, inflammation, and skin infections.Such knowledge and habits are passed down from generation to generation.The use of these medicinal plants reflects the community's dependence on the local ecosystem as a provider of natural resources that are important for health.This research serves as a foundation for the protection and sustainable utilization of medicinal plants.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".