Wastewater, sludge and excreta use in developing countries: an overview
Bibliographic record
Abstract
After introducing terms and terminology of wastewater, sludge and excreta use, the chapter highlights their global drivers and significance using examples from different parts of the developing world. It is useful in the discussion to differentiate between unplanned use of wastewater resulting from poor sanitation, and planned use which tries to address matters such as economic or physical water scarcity. Both types of wastewater use can have significant socio-economic benefits but also institutional challenges and risks which require different management approaches and, ideally, different guidelines. This diversity makes the current WHO Guidelines, which try to be global in nature, complex to understand and apply. Whilst planned reuse will remain the norm in countries that can afford treatment, most countries in the developing world are likely to continue to use non- or only partially treated wastewater, for as long as sanitation and waste disposal are unable to keep pace with urban population growth. However, there are options to link urban faecal sludge and wastewater management with urban food demands or other forms of resource ecovery that provide opportunities to safely close the nutrient and water loops.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".