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Record W2624354124 · doi:10.2166/washdev.2017.142

Investigation on microbial inactivation and urea decomposition in human urine during thermal storage

2017· article· en· W2624354124 on OpenAlexaff
Xiaoqin Zhou, Yajie Li, Zifu Li, Yue Xi, Sayed Mohammad Nazim Uddin, Yang Zhang

Bibliographic record

VenueJournal of Water Sanitation and Hygiene for Development · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsUniversity of Victoria
FundersBill and Melinda Gates Foundation
KeywordsUrineUreaAmmoniaAmmoniumChemistryAmmonia volatilization from ureaDecompositionUreaseHuman healthNitrogenThermal decompositionAmmonium sulfateEscherichia coliFecesFood scienceChromatographyEnvironmental chemistryBiochemistryMicrobiologyBiologyMedicineOrganic chemistryEnvironmental health

Abstract

fetched live from OpenAlex

The World Health Organization suggests storing human urine for at least 6 months at 20 °C prior to application as fertilizer to reduce the potential health risks from pathogenic organisms. Such a storage condition for human urine, however, not only requires a long period of time and large space but also ignores the risk of nitrogen losses. In this study, human urine underwent thermal treatment during storage to improve disinfection and to inhibit urea hydrolysis. Microbial indicators such as Escherichia coli and fecal coliforms and the concentration of ammonia/ammonium were investigated in urine samples that were stored at 60 °C and 70 °C. Both the inactivation of indicators and decomposition of urea improved under storage temperatures of 60 °C and 70 °C compared with storage under ambient temperature. Therefore, human urine is recommended to be stored at 70 °C for 7 days for hygienic and stabilization purposes. Under this storage condition, pH is maintained below 8.0 and ammonia/ammonium content is maintained at approximately 800 mg/L.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.245

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 head0.023
GPT teacher head0.250
Teacher spread0.226 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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