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Record W2957396029

Process cost analysis for the optimization of a container-based sanitation service in Haiti

2018· article· en· W2957396029 on OpenAlexfundno aff
Claire Remington, Leah Nevada Page Jean, Sasha Kramer, J. Boys, Caetano C. Dorea

Bibliographic record

VenueLoughborough University Institutional Repository (Loughborough University) · 2018
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsnot available
FundersGrand Challenges CanadaSall Family FoundationEidgenössische Anstalt für Wasserversorgung Abwasserreinigung und GewässerschutzInter-American Development Bank
KeywordsSanitationBusinessService (business)RevenueMindsetLivelihoodEnvironmental economicsMarketingFinanceEconomicsEngineeringEnvironmental engineeringAgricultureComputer science
DOInot available

Abstract

fetched live from OpenAlex

A process cost analysis methodology was developed to calculate the per capita operational costs of the container-based sanitation service in Haiti operated by the non-profit research and development organization Sustainable Organic Integrated Livelihoods (SOIL). SOIL’s sanitation service covers the entire sanitation value chain, including containment, collection, transport, treatment and reuse. The results showed that around 30% of the service’s operational costs were covered with operating revenue. The researchers then used the detailed results to identify productive areas for cost reduction and further innovation. Findings also contributed to the development of a hybrid funding model that will enable increased access to sanitation while building relationships with public institutions and reinforcing a business mindset to encourage cost-effectiveness with scale.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.199
Teacher spread0.187 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2018
Admission routes1
Has abstractyes

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