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Record W2778513765 · doi:10.14796/jwmm.c434

Greywater Quality Changes in Laboratory Scale Vegetative Biofiltration Prototypes

2017· article· en· W2778513765 on OpenAlexvenueno aff
Rezaul Chowdhury, Jameelu S. Abaya, Taoufik Tsiksi, Mohamed M. Mohamed

Bibliographic record

VenueJournal of Water Management Modeling · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
FundersNational Water Center, United Arab Emirates UniversityUnited Arab Emirates University
KeywordsGreywaterReuseEnvironmental scienceBiofilterWastewaterEnvironmental engineeringWaste managementEngineering

Abstract

fetched live from OpenAlex

Greywater recycling and reuse is becoming a popular choice in many water-scarce regions. Greywater is the largest component of domestic indoor wastewater. However, in many arid regions, including the United Arab Emirates (UAE), this unconventional resource is under-utilized. Although, traditionally, a biofiltration system operates using stormwater, findings have shown that the operation of biofiltration systems in arid regions can be successful using greywater. This research endeavors to assess how the quality of the effluent from a biofiltration system changes with different vegetation, both native and introduced to the arid region (UAE). The prototype biofilters, planted with twelve ornamental plant species, were irrigated daily with an equal amount of synthetic greywater and the water quality parameters were monitored on a weekly basis. Analysis of variance (ANOVA) was performed in order to statistically evaluate the consistency of results between the three replicates of each of the twelve species and, subsequently, the variation of treatment effectiveness among the twelve species with respect to eight water quality parameters.

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)0.001
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 score0.490
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.275
Teacher spread0.248 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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