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Record W2126181372 · doi:10.1139/l09-042

Membrane concentrate management options: a comprehensive critical reviewA paper submitted to the Journal of Environmental Engineering and Science.

2009· article· en· W2126181372 on OpenAlexaffvenue
Pamela Chelme‐Ayala, Daniel Smith, Mohamed Gamal El‐Din

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicFreezing and Crystallization Processes
Canadian institutionsUniversity of Alberta
FundersBureau of Reclamation
KeywordsNanofiltrationReverse osmosisNatural organic matterWater qualityMembrane technologyPollutantWater treatmentWaste managementEnvironmental scienceMembraneEngineeringEnvironmental engineeringChemistry

Abstract

fetched live from OpenAlex

Membrane processes have become a competitive option to conventional treatment technologies because of the high quality of the product water. In particular, nanofiltration and reverse osmosis have been found to remove materials like natural organic matter, disinfection by-products, and endocrine-disrupting compounds. However, an issue identified as one of the major drawbacks for the adoption of pressure-driven membrane processes is the need for additional treatment of the concentrate stream. Few studies dealing with membrane concentrate treatment have been published. The majority of the published studies address the disposal of concentrate into receiving waters bodies and sewer systems. In this review paper, the characteristics of membrane concentrate in terms of water quality and their impact on receiving water bodies are discussed. In addition, several approaches to the removal of pollutants and disposal options for membrane concentrates are examined.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations72
Published2009
Admission routes2
Has abstractyes

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