Community-driven assessment of springs and ponds: Status, dependency, and utilization in Kavre district, central Nepal
Bibliographic record
Abstract
Effective management of spring water sources is crucial for ensuring a sustainable water supply in mountainous regions. This study employed a community-based approach, leveraging citizen science and local knowledge to map and assess springs and ponds across seven municipalities in Nepal. Community Resource Persons (CRPs), trained and equipped with the Survey123 app, played a pivotal role in data collection. This approach generated one of the most comprehensive, community-driven spring inventories in the region, directly addressing critical gaps in scalable and standardized documentation. The effort resulted in mapping 5689 water sources, including 5168 springs and 521 ponds, highlighting their distribution and condition. The research underscores the socio-economic and environmental significance of these water sources, supporting diverse uses from drinking and agriculture to cultural practices. However, 27 % of documented sources have dried up due to earthquakes, droughts, and infrastructure development, indicating increasing water scarcity at the community level. Despite challenges, communities exhibit resilience, implementing adaptation measures like harvesting rainwater and identifying new water sources. Notably, only 12 % of active sources are managed by dedicated spring or water management committees, emphasizing the need for community-driven governance and gender-inclusive leadership. This research, involving continuous consultation with local authorities and community representatives, facilitated efficient mapping of spring resources and prioritized springshed management funding in municipal plans, aligning with global sustainable development goals and providing a framework for future sustainable springshed management efforts. The comprehensive dataset is crucial for informed decision-making and sustainable water resource management, underscoring the need for integrated approaches to mitigate threats, preserve these vital natural assets, and secure water availability for future generations in central Nepal's mountain 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 | 0.000 | 0.002 |
| 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 | 0.002 |
| 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".