Conservation and Economic Impact of Damar Trees in Damar Park on the Island of Sumatra, Indonesia
Bibliographic record
Abstract
Damar Park Krui is an agroforestry land managed by the Krui community on the West Coast, Lampung, with the dominance of damar trees (Shorea javanica).This research aims to analyze the vegetation and economic contribution of Damar Park.The research method includes making measuring plots and collecting vegetation data in Pekon Pahmungan and Pekon Gunung Kemala, and interviews with 100 respondents in June-July 2024.The tools used included Christen meters, tape measures, and GPS.Vegetation data analysis was carried out using the Importance Value Index (IVI).The results show that damar trees dominate with the largest number of trees, the largest base area, and the highest IVI.In Pekon Pahmungan, there are 17 species of trees with 196 trees, while Pekon Gunung Kemala has 12 species of trees with 194 trees.A comparison of data from 2021 to 2024 shows fluctuations in the number of damar trees, with a significant increase in 2023 and a decrease in 2024.Damar Park contributes significantly to the local economy through the export of damar resin.100% of the community believes that Damar Park can help improve family economies.59% of the community is aware that the price of damar can be increased through post-harvest technology, enhancing its economic value.However, challenges such as land conversion and lack of interest from the younger generation are a concern.This research highlights the importance of damar trees in the ecosystem and local economy as well as the need for conservation and community empowerment for the sustainability of Damar Park.Future research will focus on the repong ecosystem and aim to provide local communities with knowledge about postharvest technologies that can increase prices and enhance the community's economy.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".