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Membrane Processes

2017· review· en· W4249811058 on OpenAlexaff
Marie‐Laure Pellegrin, Sara Arabi, Jorge Aguinaldo, Mary E. Sadler, Anthony D. Greiner, Lokesh P. Padhye, Marie S. Burbano, Joseph Wong, Fraser C. Kent, Andrew Dow, Bryce Danker, Jason Diamond, Sara Sadreddini, Robert McCandless, Leila Tootchi, Bill Schilling, Karla Kinser

Bibliographic record

VenueWater Environment Research · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsReuseMembrane bioreactorWaste managementEnvironmental scienceMembrane foulingActivated sludgeMembrane technologyMembrane reactorPollutantMembraneBioreactorEnvironmental engineeringFoulingBiochemical engineeringSewage treatmentEngineeringChemistry

Abstract

fetched live from OpenAlex

This review, for literature published in 2016, contains information related to membrane processes for municipal and industrial applications. This review is a subsection of the Treatment Systems section of the annual Water Environment Federation literature review and covers the following topics: membrane bioreactor (MBR) configuration, design, nutrient removal, operation, industrial treatment, anaerobic membrane systems, reuse, microconstituents removal, membrane technology advances, membrane fouling, and modeling. Other sub-sections of the Treatment Systems section that might relate to this literature review include: Biological Fixed-Film Systems, Activated Sludge and Other Aerobic Suspended Culture Processes, Anaerobic Processes, and Water Reclamation and Reuse. The following sections 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 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)0.001
metaresearch head score (Gemma)0.001
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 score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.013

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 head0.210
GPT teacher head0.395
Teacher spread0.186 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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