Demand Satisfaction as a Framework for Understanding Intermittent Water Supply Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Nearly one billion people worldwide receive water through piped networks that are not continually pressurized and operate intermittently. The prevalence and persistence of these Intermittent Water Supplies (IWS) is surprising as this mode of operation induces water contamination and customer equity issues. Shortages of source water, customers' water demand, and leaking pipes are frequently cited as necessitating IWS. We propose a framework for understanding the persistence and operation of IWS. The supply system is represented by an average customer and a spatially averaged leakage rate. With this macroscopic hydraulic model, we relate customer demand satisfaction, source water availability, customer demand, and leakage. While this approach ignores the complexities of network topology, we find that the model approximates real systems well (calibrating to four intermittent reference networks achieved R 2 >0.94). The calibrated model is robust to moderate changes in demand and leakage (maintaining R 2 >0.83). Using the model, we show that the tipping point between satisfied demand and unsatisfied demand is a local optimum for utilities, which may explain the persistence of IWS. Beyond this point, the volume received by customers does not increase, but utilities must supply more water to the network. The generality of the proposed model enables its use when regulating and upgrading IWS. We demonstrate the latter by critiquing a performance‐based contract that was intended to improve an intermittent supply in India. Demand satisfaction has profound implications for hydraulics and human welfare. We propose the degree of demand satisfaction as a metric for evaluating IWS and for tracking the United Nations Sustainable Development Goal 6.1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it