Adapting to Peri-urban Water Insecurity Induced by Urbanization and Climate Change
Bibliographic record
Abstract
This paper describes the implications of growing urbanization in combination with climatic variability on water security and adaptation strategies of people in the peri-urban landscape of Kathmandu valley. Through a series of focus group discussions and key informant interviews, we found that entire households at Lubhu, Nepal depend on public stand posts with water supplied for few hours a day. Hydro-meteorological data analysis for the area showed an increasing trend of temperature, but a clear pattern in precipitation was not found. However, people perceived the changes in both precipitation and temperature and impacts on their livelihoods. People have envisioned development of a filtration system to treat water from another source. However currently, they have been fetching water from dug wells and spring sources in neighbouring VDCs during the days without water supply in stand posts. Farmers have been adapting to water scarcity by switching to less water demanding crops, by leaving land fallow, and by taking on off-farm activities. The concern for sustainable water management is growing among the community, however. Strong dedication and unity among the communities is essential to ensure the water security in the village.DOI: http://dx.doi.org/10.3126/hn.v14i0.11259HYDRO Nepal JournalJournal of Water, Energy and EnvironmentVolume: 14, 2014, JanuaryPage: 43-48
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".