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Record W1681229136 · doi:10.1002/bit.25277

Bacterial community structure and activity of sulfate reducing bacteria in a membrane aerated biofilm analyzed by microsensor and molecular techniques

2014· article· en· W1681229136 on OpenAlexafffund
Hong Liu, Shuying Tan, Zhiya Sheng, Yang Liu, Tong Yu

Bibliographic record

VenueBiotechnology and Bioengineering · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiofilmBacteriaAnoxic watersTemperature gradient gel electrophoresisChemistryAerationMicrobiologyGel electrophoresisSulfate-reducing bacteriaSulfateMicroaerophileChromatographyMembraneEnvironmental chemistryBiochemistryBiology16S ribosomal RNAGeneOrganic chemistry

Abstract

fetched live from OpenAlex

The activities and vertical spatial distribution of sulfate reducing bacteria (SRB) in an oxygen (O2 )-based membrane aerated biofilm (MAB) were investigated using microsensor (O2 and H2 S) measurements and molecular techniques (polymerase chain reaction-denaturing gradient gel electrophoresis [PCR-DGGE] and fluorescence in situ hybridization [FISH]). The O2 concentration profile revealed that O2 penetrated from the bottom (substratum) of the gas permeable membrane, and was gradually consumed within the biofilm until it was completely depleted near the biofilm/bulk liquid interface, indicating oxic and anoxic zone in the MAB. The H2 S concentration profile showed that H2 S production was found in the upper 285 µm of the biofilm, indicating a high activity of SRB in this region. The results from DGGE of the PCR-amplified dissimilatory sulfite reductase subunit B (dsrB) gene and FISH showed an uneven spatial distribution of SRB. The maximum SRB biomass was located in the upper biofilm. The information from the molecular analysis can be supplemented with that from microsensor measurements to better understand the microbial community and activity of SRB in the MAB.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.007
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.004
GPT teacher head0.188
Teacher spread0.183 · 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.

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

Citations14
Published2014
Admission routes2
Has abstractyes

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