MétaCan
Menu
Back to cohort

Key syntrophic partnerships identified in a granular activated carbon amended UASB treating municipal sewage under low temperature conditions

2020· article· en· W3028224204 on OpenAlexafffund
Yingdi Zhang, Bing Guo, Lei Zhang, Yang Liu

Bibliographic record

VenueBioresource Technology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsUniversity of Alberta
FundersAlberta InnovatesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsGeobacterMethanobacteriumMethanosaetaChemistryAnaerobic digestionMethanogenesisSewage sludgePulp and paper industryBacteriaActivated sludgeSewageMicrobial population biologyEnvironmental chemistryMethaneSewage treatmentWaste managementEnvironmental scienceEnvironmental engineeringBiologyBiofilmOrganic chemistry

Abstract

fetched live from OpenAlex

Two laboratory-scale up-flow anaerobic sludge blankets (UASB) reactors, one with and one without granular activated carbon (GAC), were operated for municipal sewage treatment at low temperatures (16.5 ± 2.0 °C). During the 120-day operation, the GAC-amended reactor significantly enhanced COD removal (from 62% to 75%, P < 0.05) and methane production (from 87 to 218 mg CH4-COD/reactor/d) than the non-GAC reactor. Bacterial communities were significantly different between the two reactors (P < 0.05). Geobacter, a key indicator for direct interspecies electron transfer (DIET), had the highest differential score (LEfSe analysis), showing significantly higher abundances in the GAC-amended reactor (3.7–8.8%) than the non-GAC reactor (0.9–4.0%). GAC also enriched syntrophic bacteria, Syntrophomonas, Syntrophus and sulfate reducing bacteria. Methanobacterium dominated the archaeal community in the GAC-amended reactor sludge (35.7%) and GAC-biofilm (75.3%), and was less abundant in the non-GAC reactor (9.9%). It indicates that GAC enriched microbial syntrophic partners with potential electro-activities in the anaerobic digestion process.

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 score0.015
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.220
Teacher spread0.203 · 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

Citations72
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueBioresource TechnologySame topicMicrobial Fuel Cells and BioremediationFrench-language works237,207