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Record W2109040005 · doi:10.1680/jees.2013.0023

Sludge pretreatment before aerobic digestion to enhance pathogen destruction

2013· article· en· W2109040005 on OpenAlexaffvenue
L. Seaman, R. Sherif, Wayne J. Parker, Kevin Kennedy, Peter Seto

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsEnvironment and Climate Change CanadaUniversity of WaterlooUniversity of Ottawa
Fundersnot available
KeywordsAerobic digestionAerationDigestion (alchemy)Anaerobic digestionChemistryActivated sludgeHydraulic retention timePulp and paper industryMicroorganismResidence time (fluid dynamics)Food scienceEnvironmental scienceEnvironmental engineeringSewage treatmentBiologyBacteriaChromatography

Abstract

fetched live from OpenAlex

Bench scale experimentation was completed to assess the potential of using a short residence time pretreatment reactor upstream of aerobic digestion to enhance the destruction of pathogens. The impact of aeration, temperature, hydraulic residence time (HRT), solids concentration, and feeding frequency on the pretreatment process was investigated. Subsequent testing evaluated pathogen destruction resulting from the operation of selected pretreatment conditions in a staged configuration with conventional aerobic digesters. Either highly oxidative or highly reductive conditions were observed to be most effective in reducing the concentrations of E. coli. and Salmonella spp. in the pretreatment reactor. When operated in series with the aerobic digesters, the more highly reducing conditions in pretreatment were found to enhance die-off of the microorganisms in subsequent aerobic digestion compared to the control.

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 score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.003
GPT teacher head0.213
Teacher spread0.210 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

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