Socio-Economic Carrying Capacity of the Poleang Watershed Area Indonesia
Bibliographic record
Abstract
Watersheds are areas that hydrologically have the capacity to drain water, conservation areas, drain water gradually, maintain water quality and reduce mass discharges, and can also be utilized for socio-economic purposes.Improper management and over-utilization of natural resources in the watershed can lead to damage and criticality of the surrounding area.As is the case with the Poleang watershed, due to population growth and activities to fulfill economic needs, it is able to change the function of forests in the Poleang watershed area to other uses that can reduce the quality of the Poleang watershed, therefore monitoring and evaluating watershed management performance is needed.Watershed monitoring and evaluation is carried out to assess watershed support capacity based on the watershed monitoring and evaluation method according to PERMENHUT RI No. P.61/MENHUT-II/2014.In this study, the performance of the Poleang watershed was analyzed by assessing the performance of the watershed based on its socio-economic carrying capacity.Based on the results of the analysis of socio-economic carrying capacity parameters, it was found that social criteria in the form of population pressure on agricultural areas were in the high category, from economic criteria it was found that the population welfare index was in moderate condition, and institutions through the existence and enforcement of laws in the good category.Based on the assessment of the condition of these three criteria, the socio-economic carrying capacity of the Poleang watershed is in the good category with a value of 83.75.which means that the socio-economic conditions of the community are classified as good with the current condition of the Poleang watershed.
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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 | 0.001 |
| 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 | 0.001 | 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".