Assessment of the potential contribution of alternative water supply systems in two contrasting locations: Lilongwe, Malawi and Sharm El-Sheikh, Egypt
Bibliographic record
Abstract
Abstract Growing water demand poses a challenge for supply. Poor understanding of alternative sources can hamper plans for addressing water scarcity and supply resilience. The potential of three alternative supply systems in Lilongwe, Malawi and Sharm El-Sheikh, Egypt are compared using a fast, data-light assessment approach. Lilongwe water supply is based on unsustainable use of source water, while Sharm depends primarily on desalination. Both locations experience shortages due to poor system performance and service inequity. Alternative supply systems are shown to potentially contribute to supply augmentation/diversification, improving service and system resilience. There are considerable seasonal variations to consider, especially regarding storage of water. Social preferences could limit the uptake/demand for alternative water. One important conclusion is the value in addressing public perceptions of alternative systems, and assessing water end use in order to site systems appropriately. Other issues surround financing, encouraging uptake and addressing institutional/governance aspects surrounding equitable distribution. A further consideration is whether demand reductions might yield shorter-term improvements in performance without the need to institute potentially expensive alternative water strategies. Reducing non-revenue water is a priority. Such measures should be undertaken with alternative supply enhancement to reduce inequity of supply, improve system performance and increase resilience to future changes.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".