Economic-Ecological Values of Non-Tidal Swamp Ecosystem: Case Study in Tapin District, Kalimantan, Indonesia
Bibliographic record
Abstract
This article would like to describe the economic and ecological benefits, as well as analyzing the total economic value of non-tidal swamps. Non-tidal swamps in the District Tapin are interested to be studied because of the use of non-tidal swamp in this area for people, especially ethnic Banjar since over a hundred years ago. But since 2011 the South Kalimantan local government has set a palm oil plantation development plans (Elaeis guineensis) on the area. Assessment has been conducted with a total valuation approach (total valuation). We found that the ecosystem has economic benefits in the form of functions of water supply for rice paddy (Oryza sativa), timber plants (Melaleca cajuputi), fisheries, Purun plants (Eleocharis dulcis), and functions as a source of domestic water. It has also ecological benefits in the form of biological functions such as: the provision of feed (feeding ground), where fish rearing, timber Galam (nursery ground), and hatchery fish (spawning ground), as storage and recycle of water, and function options (option value) in the form of biodiversity. Based on the results of the assessment are known, the total economic value amounted to 22.7 million per hectare, with the ratio of the economic value of only 7.14% compared to the ecological value of 92.86%. Therefore non-tidal swamps conversion plan into another function not only the loss of economic value (direct benefits) that had been in the swamp enjoy the surrounding community, but also a greater loss in the form of loss of ecological value (indirect benefits).
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".