Citizen science approach for springshed management: A comprehensive community-driven mapping and dataset of spring sources in Kavre, Nepal
Bibliographic record
Abstract
In the Himalayan region, a reliable and comprehensive spring source dataset is crucial for sustainable water source management, protection of the environment, climate change adaptation efforts, and formation of evidence-based informed policy decisions, where springs play a vital role in local water security. Community Resource Persons (CRPs)- these are local community members, who were trained in springshed management as well as in field data collection and collected the data using a mobile-based ArcGIS Survey123 app. The dataset includes the spring's georeferenced location, water use patterns, source conditions, discharge, basic socio-economic characteristics of dependent communities, and adaptation measures implemented by communities to address water scarcity. By integrating local knowledge and citizen science approaches with state-of-the-art digital technology, the dataset offers valuable insights for sustainable water resource management, highlighting the biophysical, cultural, social, and governance aspects of springs in the drought-prone region. This data can support socio-ecological research inform policymaking targeting water security and efforts aimed at the revival of springs. As the dataset is made publicly available through ICIMOD's Regional Database System (RDS) (https://rds.icimod.org/), it will serve as a foundation for the identification of critical springs for long-term monitoring and revival and assessing the impact on water security after the revival of dried springs, which will be important for researchers, policymakers, and conservation practitioners alike.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".