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Record W1856430005 · doi:10.2166/wrd.2015.126

Water reuse perceptions of students, faculty and staff at Western University, Canada

2015· article· en· W1856430005 on OpenAlexaffabout
D. Velasquez, Ernest K. Yanful

Bibliographic record

VenueJournal of Water Reuse and Desalination · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsWestern University
Fundersnot available
KeywordsLand reclamationReclaimed waterReuseWastewaterWater cycleWater resourcesWater qualityEnvironmental scienceEnvironmental planningBusinessEnvironmental engineeringGeographyEngineeringWaste managementEcology

Abstract

fetched live from OpenAlex

Global fresh water resources are under increasing pressure from rapidly growing demands and changing climatic conditions. Wastewater reclamation is becoming an important alternative for sustainable water resources management and building climate change resiliency in many regions around the world. Public acceptance and trust of consumers in the quality of reclaimed water is considered by many to be the most important factor determining the outcomes of water reclamation projects. Knowledge of the urban water cycle and water reuse perceptions of student, faculty and staff at Western University were investigated. Results showed that members of the university community are more likely to accept reclaimed wastewater for applications that do not involve drinking or close personal contact. Knowledge of the urban water cycle and water resources in Canada is modest among the university community with a moderate (G = 0.303, p < 0.05) positive relationship between ‘water knowledge’ and ‘close contact acceptability’. The majority of the university community (75.8%) thinks that reclaiming water to provide an alternate source of water in southwestern Ontario is a good idea, but there are still concerns about the presence of chemicals such as pharmaceuticals from reclaimed water and the long-term effects on human health from exposure to these contaminants.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.996

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 science0.0000.001
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 head0.240
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

Citations19
Published2015
Admission routes2
Has abstractyes

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