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Record W2084825032 · doi:10.4296/cwrj195

Water Reuse and Recycling in Canada: A Status and Needs Assessment

2004· article· en· W2084825032 on OpenAlexvenueaboutno aff
Karl A. Schaefer, Kirsten Exall, J. Maršálek

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
Fundersnot available
KeywordsReuseGreywaterLandscapingEnvironmental planningBusinessFlexibility (engineering)Water conservationIrrigationWastewater reuseToiletWater supplyWater resourcesWater resource managementEnvironmental scienceEnvironmental economicsNatural resource economicsWaste managementEnvironmental engineeringEngineeringEconomics

Abstract

fetched live from OpenAlex

The Canadian experience with water reuse and recycling is reviewed under five theme areas: technology; policy and regulation; research; public acceptance; and coordination. At present, water reuse and recycling in Canada is practiced on a relatively small scale and varies regionally depending on the availability of water supplies and regulatory flexibility. Typical examples include using treated municipal wastewater to irrigate agricultural nonfood crops, urban parkland, landscaping and golf courses. Water recycling also exists in select industrial sectors and experimental greywater treatment and reuse for toilet flushing, irrigation or other nonpotable uses at the scale of individual buildings. Recommendations for further action are presented from a recent national experts workshop on water reuse. The interest in reuse will likely increase, driven to a large extent by steadily increasing water demands, conflict among users and opportunities to save on future expansion of water supply 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.184
Teacher spread0.176 · 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.

Study designNot applicable
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

Citations43
Published2004
Admission routes2
Has abstractyes

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