Conventional Sewer Systems Are Too Time-Consuming, Costly and Inflexible to Meet the Challenges of the 21st Century
Bibliographic record
Abstract
There is an urgent need for innovation in the sanitation sector because the conventional model (toilet-to-sewer-to-treatment) is too time-consuming and costly, and alternatives are lacking. We estimate the challenge ahead by developing scenarios for 60 of the fastest-growing urban conglomerates in the World. We find that the majority would need to build out their sewer systems at a rate that is ten to 50 times higher than the highest rate for any project in the World Bank’s database, which is unrealistic. We also carry out a case study of Lagos, Nigeria, which suggests that, in any given year, 14–37% of Lagos State’s budget would need to be invested to provide sanitation to the presently underserviced population while keeping up with population growth, which also is unrealistic. Our study provides clear evidence that the conventional model for sanitation is unworkable for rapidly growing urban areas. We conclude there is an urgent need to encourage and fund projects that promote innovations that can tackle the three core challenges: can be built sufficiently quickly, are flexible, and affordable. This is not likely to happen unless the future generation is systematically trained and educated to creatively support innovation in sustainable sanitation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".