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Record W2922530144 · doi:10.1186/s13568-019-0746-0

Characterization of the protein fraction of the extracellular polymeric substances of three anaerobic granular sludges

2019· article· ca· W2922530144 on OpenAlexafffund
Charles-David Dubé, Serge R. Guiot

Bibliographic record

VenueAMB Express · 2019
Typearticle
Languageca
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversité de MontréalNational Research Council Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMethanosaetaExtracellular polymeric substanceBiochemistryBiologyExtracellularMicrobiologyBiofilmChemistryBacteriaArchaea

Abstract

fetched live from OpenAlex

Extracellular polymeric substances (EPS) play major roles in the efficacy of biofilms such as anaerobic granules, ranging from structural stability to more specific functions. The EPS of three granular anaerobic sludges of different origins were studied and compared. Particularly, the peptides from the protein fraction were identified by mass spectrometry. Desulfoglaeba and Treponema bacterial genera and Methanosaeta and Methanobacterium archaeal genera were prominent in all three sludges. Methanosaeta concilii proteins were the most represented in EPS of all three sludges studied. Principally, four proteins found in the three sludges, the S-layer protein, the CO-methylating acetyl-CoA synthase, an ABC transporter substrate-binding protein and the methyl-coenzyme M reductase, were expressed by Methanosaeta concilii. Mainly catabolic enzymes were found from the 45 proteins identified in the protein fraction of EPS. This suggests that EPS may have a role in allowing extracellular catabolic reactions.

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 score0.003

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 head0.008
GPT teacher head0.186
Teacher spread0.178 · 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

Citations41
Published2019
Admission routes2
Has abstractyes

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