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Effect of Membranes on Refractory Dissolved Organic Nitrogen

2010· article· en· W2413262586 on OpenAlexaff
Mingu Kim, George Nakhla

Bibliographic record

VenueWater Environment Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsAnoxic watersMembrane bioreactorChemistryAerationEffluentWastewaterMembraneMembrane foulingNitrogenDissolved organic carbonFoulingEnvironmental chemistryBioreactorPulp and paper industryEnvironmental engineeringEnvironmental scienceBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

A 500-day comparative study with a novel membrane bioreactor (NMBR), anaerobic-anoxic-aerobic (A2/O) process, and University of Cape Town-adapted MBR (UMBR) investigated the effect of membrane on effluent dissolved organic nitrogen (efDON) using synthetic (SWW) and municipal wastewater (MWW). The runs, comparing an NMBR and A2/O process, indicated 0.3 mg/L lower efDON in the former than the latter. However, NMBR and UMBR achieved similar efDON quality, with an average of 0.8 mg/L, and the DON reduction by membrane averaged 0.4 mg/L, while the A2/O efDON was slightly higher than DON in the aeration tank, by 0.08 mg/L, on average. The efDON during the MWW run increased by as much as 0.8 mg/L compared with the SWW run. The efDON is a component of a protein found in soluble microbial products, and it followed a cyclical temporal pattern during the runs. Membrane fouling propensity increased the efDON. This study presents evidence that membranes are effective in reducing efDON.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.018
GPT teacher head0.295
Teacher spread0.278 · 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 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

Citations4
Published2010
Admission routes1
Has abstractyes

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