Evaluation of the Safe Water Optimization Tool to Provide Evidence-Based Chlorination Targets in Surface Waters: Lessons from a Refugee Setting in Uganda
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The Safe Water Optimization Tool (SWOT) generates evidence-based point-of-distribution free residual chlorine (FRC) targets to adjust chlorine dosing by operators and ensure water quality at point-of-consumption. To investigate SWOT effectiveness in surface waters, we conducted two before-and-after mixed-method evaluations in a Uganda refugee settlement served by piped and trucked surface water systems. We surveyed 888 users on water knowledge, attitudes, and practices; collected 2768 water samples to evaluate FRC, Escherichia coli, and disinfection by-products (DBPs) concentrations; and conducted nine key-informant interviews with system operators about SWOT implementation. After baseline data collection, SWOT chlorination targets were generated, increasing point-of-distribution FRC targets from 0.2 to 0.7–0.8 mg/L and from 0.3 to 0.9 mg/L for piped and trucked systems, respectively. At endline, household point-of-consumption FRC ≥ 0.2 mg/L increased from 23 to 35% and from 8 to 42% in the two systems. With these increases, we did not observe increased chlorinated water rejection or DBPs concentrations exceeding international guidelines. Informants reported that SWOT implementation increased knowledge and capacity and improved operations. Overall, SWOT-generated chlorination targets increased chlorine dosage, which improved household water quality in surface waters although less than previously documented with groundwater sources. Additional operator support on prechlorination water treatment processes is needed to ensure maximally effective SWOT implementation for surface water sources.
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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 | 0.076 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".