Spatial Distribution of Dryland Forest on Water Availability in Kumaligon Watershed Central Sulawesi, Indonesia
Bibliographic record
Abstract
Kumaligon Watershed in Buol Regency, Central Sulawesi Province is located in a groundwater basin with an area of 1,488.77Ha.The region consists of karst hills and dryland forest cover with high water demand.This watershed is the main source of water for the community in fulfilling the needs of clean water, agriculture and tourism, so this research is important to do.Therefore, this study aims to determine the spatial distribution effect of dryland forest area on water availability in the Kumaligon Watershed.A spatialmeteorological method was used with an analytical approach to determine the distribution, while the Thornthwaite-Mather water balance analysis assessed the water availability.The result showed that dryland forest is concentrated in the upstream region of the karst hills with an area of 1,082.43Ha and water availability of 4,332.34m 3 /year.By comparing the water demand in 2021, namely, 1,218.75m 3 /year, a criticality index of 0.28 was obtained, which indicates that the condition of the region was not critical.Based on these findings, the dryland forests in the region are expected to still have an adequate supply of water in the next 25 years.
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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 | 0.000 | 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) | 0.001 | 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".