Status Keberlanjutan Pengelolaan Das Mandar Di Sulawesi Barat, Indonesia
Bibliographic record
Abstract
Mandar River is an important cultural entity for the Mandar community in West Sulawesi, in fact, faces threats such as floods and landslides. To support the government's efforts in achieving the Sustainable Development Goals (SDGs), the management of Mandar Watershed needs to integrate the ecological, economic, social, institutional, and technological dimensions. This study aims to: (1) measure the status of the sustainability of the Mandar Watershed; and (2) identify the factors that influence the sustainability of the Mandar watershed management. This research used the method of observation, interviews, documentation study, and literature review. Data analysis used a descriptive analysis approach and Multidimensional Scaling (MDS) analysis with analysis tools suc as rapfish / rapDASMandar. The results showed that the sustainability status of watershed management in the ecological dimension was quite sustainable; on the social and institutional dimensions, it is categorized as less sustainable; and in the economic and technological dimensions, the upstream and middle Mandar sub-watersheds are categorized as less sustainable. The multidimensional sustainability status of Mandar watershed management is categorized as less sustainable. There are 13 factors that need attention to improve the sustainability status of Mandar watershed management, especially in the technological, institutional, and social dimensions.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".