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Record W2324224015 · doi:10.2166/wst.2011.047

Characteristics of different fractions of microbial flocs and their role in membrane fouling

2011· article· en· W2324224015 on OpenAlexaff
Hongjun Lin, Weijue Gao, Kam Tin Leung, Baoqiang Liao

Bibliographic record

VenueWater Science & Technology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsLakehead University
Fundersnot available
KeywordsExtracellular polymeric substanceFoulingMembrane foulingChemistryMembraneFraction (chemistry)Temperature gradient gel electrophoresisChromatographyMicrobial population biologyChemical engineeringPulp and paper industryBiofilmBacteriaBiology

Abstract

fetched live from OpenAlex

Characteristics of different fractions (small flocs vs. large flocs) of sludge flocs from a submerged anaerobic membrane bioreactor treating thermomechanical pulping (TMP) whitewater were determined using various analytic techniques, including extraction and chemical analysis of extracellular polymeric substances (EPS), particle size analyzer, and polymer chain reaction (PCR)-denaturing gradient gel electrophoresis (DGGE). The results showed that the fraction of smaller flocs contained a higher level of bound EPS and had a higher fractal dimension as compared to the fraction of larger flocs. PCR-DGGE analysis indicated that there were significant differences in microbial community between the fraction of smaller flocs and large flocs. The microbial community of the smaller flocs was similar to that of the sludge cake layers, indicating the pioneering role of the microbial community in smaller flocs in membrane fouling. These findings provide a new insight in the difference of membrane fouling potential between smaller flocs and larger flocs fraction.

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)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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

Citations55
Published2011
Admission routes1
Has abstractyes

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