The method of material flow analysis, a tool for selecting sustainable sanitation technology options: the case of Pouytenga (Burkina Faso)
Bibliographic record
Abstract
Like many other cities in Sub Saharan Africa, Pouytenga in Burkina Faso has sanitation problems characterized by poor public health. Pouytenga is located upstream of the dam of Yitenga, and the pollution of this city is drained into this dam, which is used to supply drinking water. The city has no strategic sanitation plan. So, the objective of the study is to assess the dynamics of material flows and pollutant (nitrogen) through different technological sanitation options of Pouytenga. The method of material flow analysis (MFA) is used to assess the matter and nutrients fluxes. The methodology includes literature review, household surveys and chemical analyses of wastewater and fecal sludge. Several scenarios of sanitation options have been assessed. The results show that Pouytenga currently discharges about 61,824 tons of material, including 36 tons of nitrogen per year in surface water and 373 098 tons of materials, including 282.6 tons of nitrogen on groundwater per year. The promotion of diverted urine toilets in the city and solid waste composting is expected to recover 194 tons of nitrogen for agriculture.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".