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Record W2310587286 · doi:10.2166/washdev.2016.103

Passive evaporation of source-separated urine from dry toilets: UES optimization and dry product accumulation over time

2016· article· en· W2310587286 on OpenAlexaff
David N. Bethune, Angus Chu, M. Cathryn Ryan

Bibliographic record

VenueJournal of Water Sanitation and Hygiene for Development · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTrayChemistryAmmoniaNitrogenEnvironmental chemistryWaste managementPulp and paper industryBotanyEngineering

Abstract

fetched live from OpenAlex

A urine evaporation system (UES) was optimized and evaluated in a laboratory by adding 5 L of urine at the same time each day for 65 days. The UES consisted of a wooden box that is open at the front only with tracks for 22 vertically stacked cafeteria-type trays and a fan and chimney at the back. Urine flowed from tray to tray via gravity exiting each tray via a weir along the long side of the tray. A distinctive physical and chemical zonation in the solid urine product was observed from the upper to lower trays due to leaching of precipitated minerals in the upper trays and mineral accumulation in the lower trays. The redox conditions became increasingly oxidizing from the top to bottom trays due to contact with the atmosphere thus favouring more stable mineralized forms of nitrogen (ammonium and nitrate) and sulphur (sulphate) and disfavouring the less stable and volatile ammonia, nitrogen gas and hydrogen sulphide. The quality of the fertilizer product is higher in the upper trays with higher levels of nitrogen, phosphorus and potassium, whereas the lower trays have higher levels of sodium chloride. Nitrogen losses due to ammonia volatilization were approximately 35%.

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 score0.097
Threshold uncertainty score0.218

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 head0.015
GPT teacher head0.238
Teacher spread0.223 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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