Effects of hydraulically disconnecting consumer pumps in an intermittent water supply
Bibliographic record
Abstract
We estimate 250 million people receive water using private pumps connected directly to intermittently pressurized distribution networks. Yet no previous studies have quantified the presumed effects of these pumps. In this paper, we investigate the effects of installing pressure-sustaining valves at consumer connections. These valves mimic pump disconnection by restricting flow. Installing these valves during the dry season at 94% of connections in an affluent neighborhood in Delhi, India, cut the prevalence of samples with turbidity > 4 NTU by two thirds. But considering the poor reputation of pumps, installed valves had surprisingly small average effects on turbidity (-8%; p<0.01) and free chlorine (+0.05 mg/L; p<0.001; N = 1,031). These effects were much smaller than the high variability in water quality supplied to both control and valve-installed neighborhoods. Site-specific responses to this variability could have confounded our results. At the study site, installed valves increased network pressure during 88% of the typical supply window; valves had a maximum pressure effect of +0.62 m (95% CI [0.54, 0.71]; a 40% increase vs. control). Further research is needed to generalize beyond our study site. Nevertheless, this paper provides unique evidence showing how the deployed valves mitigated pump effects, increased network pressure and improved water safety.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".