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Record W2341295538 · doi:10.2166/wqrjc.2016.040

Protein to polysaccharide ratio in EPS as an indicator of non-optimized operation of tertiary nitrifying MBBR

2016· article· en· W2341295538 on OpenAlexaff
Baisha Ren, Bradley Young, Fabio Variola, Robert Delatolla

Bibliographic record

VenueWater Quality Research Journal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBiofilmExtracellular polymeric substanceChemistryPolysaccharideNitrifying bacteriaHydraulic retention timeChromatographyMicrobiologyEnvironmental chemistryNitrificationEnvironmental engineeringBiologyBiochemistryNitrogenBacteriaEnvironmental scienceOrganic chemistrySewage treatment

Abstract

fetched live from OpenAlex

The protein (PN), polysaccharide (PS), and extracellular DNA (eDNA) percent concentrations of extracellular polymeric substances (EPS) of biofilm samples harvested from a pilot-scale nitrifying moving bed biofilm reactor (MBBR) were investigated at various operating temperatures and hydraulic retention times (HRTs). Chemically measured EPS PN/PS ratios were shown to correlate to Raman intensity ratios of amide III to carbohydrate at 362 rel. cm−1. The study also demonstrates that tertiary nitrifying MBBR systems may be optimized to operate at HRTs as low as 0.75 to 1.0 h as opposed to conventional HRTs of 2.0 to 6.0 h. The EPS of the nitrifying MBBR biofilm exhibited the lowest percent PN content and the highest percent PSs and eDNA content. In particular, PN/PS ratios lower than 3 were indicative of non-optimal operation of the nitrifying MBBR systems, whereas PN/PS ratios with values significantly below 3 were observed for ammonia underloaded systems at high operating temperatures and hydraulically overloaded systems at low HRTs. This study demonstrates that the PN/PS ratio in EPS is a potential metric to identify non-optimal operation of nitrifying MBBR systems.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.021
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.368
Teacher spread0.309 · 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 teacher head, not a consensus.

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

Citations14
Published2016
Admission routes1
Has abstractyes

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