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Record W4211205896 · doi:10.1002/wer.1385

Membrane processes

2020· review· en· W4211205896 on OpenAlexaff
Sara Arabi, Marie‐Laure Pellegrin, Jorge Aguinaldo, Mary E. Sadler, Robert McCandless, Sara Sadreddini, Joseph Wong, Marie S. Burbano, Srikanth Koduri, Karla Abella, Jeff Moskal, Sirwan Alimoradi, Yaldah Azimi, Andrew Dow, Leila Tootchi, Karla Kinser, Vishakha Kaushik, Valetta Saldanha

Bibliographic record

VenueWater Environment Research · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsJacobs (Canada)Suez (Canada)
Fundersnot available
KeywordsReuseMembrane bioreactorWaste managementMembrane foulingEnvironmental scienceSewage treatmentWastewaterMembrane technologyActivated sludgeEnvironmental engineeringBiochemical engineeringMembraneEngineeringFoulingChemistry

Abstract

fetched live from OpenAlex

This literature review provides a review for publications in 2018 and 2019 and includes information membrane processes findings for municipal and industrial applications. This review is a subsection of the annual Water Environment Federation literature review for Treatment Systems section. The following topics are covered in this literature review: industrial wastewater and membrane. Bioreactor (MBR) configuration, membrane fouling, design, reuse, nutrient removal, operation, anaerobic membrane systems, microconstituents removal, membrane technology advances, and modeling. Other sub-sections of the Treatment Systems section that might relate to this literature review include the following: Biological Fixed-Film Systems, Activated Sludge, and Other Aerobic Suspended Culture Processes, Anaerobic Processes, and Water Reclamation and Reuse. This publication might also have related information on membrane processes: Industrial Wastes, Hazardous Wastes, and Fate and Effects of Pollutants.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0240.107

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 head0.132
GPT teacher head0.377
Teacher spread0.244 · 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; both teacher heads agree on what is shown here.

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

Citations18
Published2020
Admission routes1
Has abstractyes

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