Revival of Hauz Khas Lake in Delhi: Approaches to Urban Water Resource Management in India
Bibliographic record
Abstract
The decline of urban water bodies in India needs to be arrested for sustainable water management in rapidly expanding Indian cities. Reuse of water after partial recycling can reduce environmental stress. Delhi, the Indian capital, has a number of surviving water storage structures built by successive rulers over centuries to tackle water shortage in the summer. In modern Delhi, a fourteen million plus city, water is supplied through technological networks, hastening the decline of the old water storage structures. The old lakes are choked with filth and the step-wells are heaps of rubble. The Indian National Trust for Art and Cultural Heritage (INTACH) and the Delhi Development Authority (DDA) have undertaken a project to revive a 700 year-old water body, lying dry for decades, the Hauz Khas Lake, with treated sewage water. The idea was to raise the groundwater table and restore the natural environment of the lake, a past habitat for water birds. This paper attempts to evaluate the immediate impact and the long-term sustainability of the effort through discussions with technical personnel, field observations and interviews with local residents. Manuals and progress reports of the concerned organizations are used as secondary sources. The paper also examines the views of government officials and NGOs regarding the role of other similar projects in alleviating Delhi’s water shortage.
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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 | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".