How to Model an Intermittent Water Supply: Comparing Modeling Choices and Their Impact on Inequality
Why this work is in the frame
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Bibliographic record
Abstract
Intermittent water supply (IWS) networks have distinct and complicated hydraulics. During periods without water supply, IWS networks drain, and consumers rely on stored water; when supply resumes, pipes and consumer storage are refilled. Draining, storage, and filling are not easily represented in standard modeling software. We reviewed 30 ways modelers have represented the hydraulics of IWS in open-source modeling tools and synthesized them into eight distinct methods for quantitative comparison. When selecting methods, modelers face two critical choices: (1) whether to ignore the filling phase, and (2) how to represent consumers as attempting to withdraw their demand: as fast as possible (unrestricted), as fast as possible until a desired volume is received (volume-restricted), or just fast enough to receive a desired volume by the end of supply (flow-restricted). We quantify these choices’ impact on consumer demand satisfaction (volume received/volume desired) and inequality using three test networks under two supply durations, implemented in two different hydraulic solvers (EPANET and EPA-SWMM). Predicted inequality and demand satisfaction were substantially affected by the choice to represent consumer withdrawals as unrestricted, volume-restricted, or flow-restricted, but not by the specific implementation (e.g., three different flow-restricted methods agreed within 0.01%). Volume-restricted methods predict wider inequalities than flow-restricted methods and unrestricted methods predict excessive withdrawal. Modeling filling delayed water provision unequally, reducing the volume received by some consumers (by ∼20%), especially where water supply is brief. All else being equal, we recommend using volume-restricted methods, especially when modeling system improvements, and including the filling process when studying inequalities.
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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 | 0.000 | 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 | 0.000 |
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 it