Water availability, consumption and sufficiency in Himalayan towns: a case of Murree and Havellian towns from Indus River Basin, Pakistan
Bibliographic record
Abstract
Abstract This study attempts to analyse the status of water availability, consumption and sufficiency in two Himalayan towns – Murree and Havellian from Pakistan's Indus Basin, using the primary data collected from 350 households, 26 town level focus groups and key informants. Findings revealed that groundwater is the main source of water on which around 85% of households are heavily dependent. Estimates of water availability, consumption and water sufficiency ratio (WSR) show that available groundwater is not sufficient (0.89) to meet the requirements for consumption in Havellian. However, in the case of Murree, available water is sufficient (1.92). Taking into account the national standards of water consumption, WSR estimates show that both towns have insufficient water availability (Murree: 0.68, Havellian: 0.50). There is evidence that in both towns, water is being mismanaged at household level. In addition, one-fifth of households reported that climate change has also affected the water availability in the towns over time. Factors such as rapid urbanization and population growth are likely to result in increased requirements of water in the future. Based on the findings, the study has suggested policy actions on protection, efficient use, diversification and governance of groundwater resources.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".