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Record W3010557950 · doi:10.2166/washdev.2020.120

Treatment of fecal matter by smoldering and catalytic oxidation

2020· article· en· W3010557950 on OpenAlexaff
Shadi Saberi, Kasra Samiei, Ewa M. Iwanek, Samoil Vohra, Masoumeh Farkhondehkavaki, Yu‐Ling Cheng

Bibliographic record

VenueJournal of Water Sanitation and Hygiene for Development · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsUniversity of Toronto
FundersBill and Melinda Gates Foundation
KeywordsSanitationSewageEnvironmental scienceFecesWaste managementHuman fecesSewage treatmentEnvironmental engineeringEcologyEngineeringBiology

Abstract

fetched live from OpenAlex

Abstract There is a strong need for transformative sanitation systems in the areas of the world where open defecation habits and/or inadequate sewage treatment methods and facilities exist. This paper describes an innovative thermally efficient solid waste treatment process as a basis for an off-the-grid, non-sewered toilet in order to address this need. Human feces are combusted in a continuous-cyclic manner using two stages of smoldering and catalytic oxidation. It has been shown that thermal coupling of the two stages creates a self-sustained reactor that can combust wet fecal material containing up to 3.2 parts water to 1 part dry matter – equivalent of water content in healthy human feces – without the need for external heating, known as the ultimate challenge in direct combustion of human feces. Furthermore, it has been shown that air flow rate can be reliably used as a controlling mechanism for fecal destruction rate which means the same reactor could be operated for various and varying input rates. The present work demonstrates the potential for manufacturing low-cost, low-energy consuming sanitation systems that are more easily accessible to communities in need of such systems.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.018
GPT teacher head0.219
Teacher spread0.201 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations15
Published2020
Admission routes1
Has abstractyes

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