Learning from intermittent water supply schedules: Visualizing equality, equity, and hydraulic capacity in Bengaluru and Delhi, India
Bibliographic record
Abstract
Intermittent distribution affects one in five piped water users, threatens water quality, and magnifies inequity. Research and regulations to improve intermittent systems are hindered by system complexity and missing data. We created four new methods to visually harness insights from intermittent supply schedules and demonstrate these methods in two of the world's most complicated intermittent systems. First, we created a new way to visualize the varieties of supply continuities (hours/week of supply) and supply frequencies (days between supplies) within complicated intermittent systems. We demonstrated using Delhi and Bengaluru, where 3278 water schedules vary from continuous to only 30 minutes/week. Second, we quantified equality based on how uniformly supply continuity and frequency were divided between neighbourhoods and cities. Delhi provides 45 % more supply continuity than Bengaluru, but with similar inequality. Bengaluru's infrequent schedules require consumers to store four times more water (for four times longer) than in Delhi, but Bengaluru's storage burden is more equally shared. Third, we considered supply inequitable where affluent neighbourhoods (using census data) received better service. Neighbourhood wealth was inequitably correlated with the percent of households with piped connections. In Bengaluru, supply continuity and required storage were also inequitably divided. Finally, we inferred hydraulic capacity from the coincidence of supply schedules. Delhi's highly coincident schedules result in city-wide peak flows 3.8 times their average - sufficient for continuous supply. Bengaluru's inconvenient nocturnal schedules may indicate upstream hydraulic limitations. Towards improved equity and quality, we provided four new methods to harness key insights from intermittent water supply schedules.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".