Community-Based Adaptation Strategies in Response to Clean Water Scarcity in a Peatland Ecosystem: A Case Study of Banyuasin, South Sumatra, Indonesia
Bibliographic record
Abstract
Indonesia contains approximately 36% of the world's tropical peatlands, which are crucial for carbon storage, biodiversity conservation, and hydrological regulation.However, in many peatland regions, including Muara Sugihan in Banyuasin Regency, South Sumatra, communities face chronic water quality problems due to peat soils rich in organic matter and high acidity.The local groundwater is bitter, salty, yellowish-brown, and pungent, making it unsuitable for human consumption.This qualitative case study applied cross-generational sampling in 22 villages to capture diverse perspectives on water adaptation.Primary data were collected through semi-structured interviews, participant observation, and field documentation, while secondary data from NGOs were prioritized for contextual relevance.Thematic analysis was used to identify patterns and variations in adaptation strategies.All respondents deemed groundwater unfit for drinking, leading to three main adaptation strategies: (1) simple filtration using sand and charcoal, (2) diversification of water sources via rainwater harvesting and selective swamp water use, and (3) boiling rainwater before consumption.Approximately 70% of younger residents have adopted technological solutions, such as large-capacity rainwater storage, deep-well drilling, and self-managed treatment, although with limited success.Older generations relied more on traditional practices.Adaptive capacity is constrained by limited filtration technology, poor infrastructure, and socioeconomic barriers.Critically, 85% of respondents reported no formal water quality monitoring, revealing a governance gap.Strengthening peatland water resilience requires expanding access to effective filtration, improving infrastructure, and instituting regular monitoring.Lessons from Muara Sugihan can inform policies for other peatland communities.
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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 | 0.001 | 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 | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".