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Record W2090531202 · doi:10.1139/s07-034

Strategic pathways for the sustainable management of water treatment plant residuals

2008· article· en· W2090531202 on OpenAlexafffundvenue
Margaret E. Walsh, Craig B. Lake, Graham A. Gagnon

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsResidualWater qualityStewardship (theology)BusinessEnvironmental resource managementEnvironmental planningSustainabilityGovernment (linguistics)Environmental scienceEnvironmental economicsComputer scienceEcologyEconomics

Abstract

fetched live from OpenAlex

In North America, the rapid movement away from the direct discharge of water treatment plant (WTP) residual streams to receiving environments has resulted in considerable benefits to the drinking water industry in terms of enhancing environmental stewardship practices and supporting source water protection strategies. Over the past 20 years, application based research on appropriate technologies to treat residual streams such as filter backwash water (FBWW) has promoted the development of integrated process design that presents recycling as a viable residual management option. However, as utilities continue to develop and expand their main process lines to comply with more stringent government regulations encompassing both water quality and residual disposal practices, the development of sustainable residual management practices is expected to become a more prominent issue for this industry. The purpose of this paper is to present, from a critical point of view, the matrix of issues that currently exist regarding WTP residual streams and strategic pathways that would enhance future decision making processes for achieving long-term residual management solutions for the drinking water industry.

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 score0.295
Threshold uncertainty score0.193

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 head0.013
GPT teacher head0.179
Teacher spread0.166 · 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

Citations21
Published2008
Admission routes3
Has abstractyes

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