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Record W2885896567 · doi:10.1155/2018/4634898

Bioaugmentation with Mixed Hydrogen-Producing Acetogen Cultures Enhances Methane Production in Molasses Wastewater Treatment

2018· article· en· W2885896567 on OpenAlexaff
Shuo Wang, Jianzheng Li, Guochen Zheng, Guocheng Du, Ji Li

Bibliographic record

VenueArchaea · 2018
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversity of Calgary
FundersFundamental Research Funds for the Central UniversitiesMajor Science and Technology Program for Water Pollution Control and TreatmentHarbin Institute of TechnologyState Key Laboratory of Urban Water Resource and EnvironmentNational Natural Science Foundation of China
KeywordsBioaugmentationWastewaterAcetogenesisChemical oxygen demandChemistryPulp and paper industryAnaerobic digestionSewage treatmentEnvironmental chemistryFood scienceWaste managementMethaneEnvironmental engineeringMicroorganismBiologyBacteriaEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrogen-producing acetogens (HPA) have a transitional role in anaerobic wastewater treatment. Thus, bioaugmentation with HPA cultures can enhance the chemical oxygen demand (COD) removal efficiency and CH4yield of anaerobic wastewater treatment. Cultures with high degradation capacities for propionic acid and butyric acid were obtained through continuous subculture in enrichment medium and were designated as Z08 and Z12. Bioaugmentation with Z08 and Z12 increased CH4production by glucose removal to 1.58. Bioaugmentation with Z08 and Z12 increased the COD removal rate in molasses wastewater from 71.60% to 85.84%. The specific H2and CH4yields from COD removal increased by factors of 1.54 and 1.63, respectively. Results show that bioaugmentation with HPA-dominated cultures can improve CH4production from COD removal. Furthermore, hydrogen-producing acetogenesis was identified as the rate-limiting step in anaerobic wastewater treatment.

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)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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