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Record W2087886978 · doi:10.1504/ijewm.2014.064083

Regrowth of bacterial pathogen indicators after advanced electro-osmotic dewatering of biosolids

2014· article· en· W2087886978 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Environment and Waste Management · 2014
Typearticle
Languageen
FieldEngineering
TopicElectrokinetic Soil Remediation Techniques
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiosolidsDewateringPathogenMicrobiologyChemistryEnvironmental sciencePulp and paper industryEnvironmental engineeringBiologyEngineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

Applying an electrical field for 10 min to mechanically dewatered biosolids increased dryness to 35 to 45% and reduced Escherichia coli levels to/close to detection limits. Possible reasons for the regrowth of bacterial pathogens impacted by electro-osmotic dewatering are biosolids dryness, low pH and the generation of bacterial oxidants on the anode due to high temperatures. Testing for this with aerobic and anaerobic incubations resulted in more than four logs of E. coli regrowth in 2011, when biosolids harboured higher initial counts; whereas regrowth was not observed in 2012, when biosolids had lower E. coli counts. In 2011 experiments, however, addition of a nitrate salt prior to anaerobic incubations reduced regrowth by 1.4 logs. This indicates that other NO3−-respiring organisms could be competing with E. coli and diverting electrons away from the E. coli population. These results suggest that electro-osmotic dewatering and nitrate can play a role in controlling E. coli regrowth.

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.

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 score0.183
Threshold uncertainty score0.360

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 head0.001
GPT teacher head0.164
Teacher spread0.163 · 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