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Record W1982991037 · doi:10.2495/wrm110601

The method of material flow analysis, a tool for selecting sustainable sanitation technology options: the case of Pouytenga (Burkina Faso)

2011· article· en· W1982991037 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueWIT transactions on ecology and the environment · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Environmental Studies
Canadian institutionsnot available
FundersEidgenössische Anstalt für Wasserversorgung Abwasserreinigung und GewässerschutzInternational Development Research Centre
KeywordsSanitationEnvironmental scienceMaterial flow analysisGroundwaterEnvironmental engineeringPollutionWastewaterWater resource managementAgricultureWaste managementEngineeringGeography

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.531
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.240
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it