Dynamics of urban water supply management of two Himalayan towns in India
Bibliographic record
Abstract
Abstract Many towns in the Indian Himalayan Region (IHR) are experiencing permanent water crises due to increasing population pressure, urbanization, and poor management of existing water sources. This paper focuses on two towns – Mussoorie and Devprayag in the western IHR – to understand various aspects of the growing water scarcity and urban water management. In the current scenario of a changing climate, natural springs, their main water resource, are drying up. Mussoorie experiences an acute shortage of water in summer, precisely when the town hosts numerous tourists. In Devprayag, religious tourism and in-migration from rural areas contribute to rising demand. The reduced discharge in nearby streams has widened the demand–supply gap. An integrated management of water sources is crucial to solving water problems in Mussoorie and Devprayag. In both towns, little effort has been made towards recharging existing water sources. Detailed planning of the water supply system while being mindful of the floating population, a proper sewage and storm water management system, and rainwater harvesting schemes, are absent. There is an urgent need to adopt a comprehensive approach to solving urban water issues, covering aspects of demand, supply and water resources management in these hill towns for adaptive water management.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".