Equality in Unrestricted Intermittent Water Supply Networks: Conceptual Model
Bibliographic record
Abstract
Up to a billion people receive drinking water intermittently, sometimes for just a few hours per week. Improving water quality and reducing inequality in these supply networks can be difficult because operational details and the configuration and condition of pipes in these networks are often uncertain. This can make it challenging to model these water networks using traditional, deterministic hydraulic models that rely on detailed information about each pipe, its diameter, and its roughness. To mitigate this challenge, this paper explores the performance of a simple, generalized, pressure-dependent model of intermittent water supply networks that does not rely on detailed information. To ensure this model has some relevance to traditional modeling approaches, the new model was validated by comparing its performance to three benchmark networks with unrestricted demands modeled by EPANET. Using the new model, we showed that consumers close to the source of supply almost always received a higher fraction of their desired water demand than those further away. Increasing supply pressure did little to reduce water inequality but could increase the total amount of water supplied. Pipes that flow to higher elevations worsened inequality among consumers in this pressure-dependent, unrestricted demand model. Efforts to reduce inequality may benefit from a focus on consumers at higher elevations. Restricting demand of users close to the source of supply may also free up water for downstream users.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".