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Record W2765121055 · doi:10.2166/wrd.2017.020

Towards development of a standard methodology for testing field performance of residential greywater reuse systems: case study of a greywater reuse system installed in 22 homes in Southern Ontario (Canada)

2017· article· en· W2765121055 on OpenAlexaffabout
Madeleine Jean Craig, Russell Richman

Bibliographic record

VenueJournal of Water Reuse and Desalination · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGreywaterReuseShowerEngineeringSubsidyEnvironmental engineeringCivil engineeringEnvironmental scienceWaste management

Abstract

fetched live from OpenAlex

Abstract Using shower wastewater to flush toilets decreases the potable water demand of residential buildings, reducing pressure on existing water supplies. ‘Off- the-shelf’ greywater reuse systems intended for single-family residential dwellings have recently become commercially available, but have variable field performance. A standard field testing methodology was developed and applied to a greywater reuse system installed in 22 homes in Southern Ontario. Performance was quantified by measuring the water balance, water quality, energy consumption, durability, maintenance requirements, installation process, economics and user satisfaction with the system. The tested system was found to save, on average, 40.9 litres per household per day, occasionally meet water quality guidelines and generally have less maintenance and durability issues than previous generations, resulting in satisfied users. However, due to low water rates and high capital costs, there is a need for government subsidization of these systems which will ultimately reduce pressure placed on centralized water infrastructure.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.053
GPT teacher head0.275
Teacher spread0.222 · 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 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

Citations12
Published2017
Admission routes2
Has abstractyes

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