Sanitation infrastructure and faecal flow – SanIFFlow: A spatial mapping tool for integrated planning and management of sanitation in unsewered urban areas
Bibliographic record
Abstract
Proper sanitation is vital for public health, particularly in urban areas. However, planning and managing sanitation systems in secondary cities within economically developing countries presents persistent challenges, largely due to a lack of spatial understanding and representation. To address these challenges, this study introduces SanIFFlow (Sanitation Infrastructure and Faecal Flow), a spatial analytical approach focused on a city-scale, ward-level model. SanIFFlow provides an actionable insights into infrastructural attributes and faecal flow dynamics, tailored to the practical governance capacities of the city’s existing management framework. By leveraging open-source data on buildings, population, and drainage network, the method offers a detailed spatial representation of faecal matter sources and movement pathways within urban catchments. This approach enables strategic sanitation planning and proactive management, identifying high-risk areas and supporting targeted interventions, such as ward-level infrastructure upgrades. SanIFFlow represents a scalable, data-driven tool designed to enhance urban sanitation management in resource-constrained settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".