The periurban water security problem: a case study of Hyderabad in Southern India
Bibliographic record
Abstract
The 2011 Census of India produced some interesting facts about the process of urbanisation in India. For the first time since Independence, the absolute increase in population is more in urban than in rural areas. The increase in urban areas has put pressure on the basic infrastructure, including access to water for both urban and periurban locations. Most Indian cities have formal water supply for only a few hours a day and only in limited areas. The question is – where are the remaining water requirements coming from? For much of India's ‘water history’, the focus has been on large-scale surface-water projects to provide access, focusing more on irrigation and neglecting sources within the city and the periurban areas. Over time an enormous informal groundwater market has arisen in several cities to bridge the demand–supply gap. This water demand is met through supplies of water through informal water markets. Water is sourced from the periurban regions, which are usually richer in surface water and groundwater. This paper focuses on the change process as witnessed by periurban areas with a case study of Hyderabad. This paper presents an overview of a trend that is leading to immense water insecurities due to a combination of issues.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".