Calculating Economic Valuation of Mangrove Forest in Bengkalis Regency, Indonesia
Why this work is in the frame
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Bibliographic record
Abstract
This research aims to determine the total economic value of mangrove forests on Bengkalis Island, Bengkalis Regency, Indonesia. Data collection in the field was done through observation and interviews. The research was carried out from October 2020 to March 2021. The results showed that the total economic value of mangrove forests was US$ 5.8 million per year. This study uses Total Economic Value (TEV) as a framework for estimating the value of the benefits of mangrove forests in Bengkalis. In addition, another value taken into account in this study is the function of mangroves as abrasion barrier, barrier to seawater intrusion, and a provider of nutrients for marine biota. The results of this study underscore the importance of mangrove ecosystems for the economy of coastal areas.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it