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Record W2323400344 · doi:10.2166/wqrjc.2016.024

Semi-continuous mesophilic anaerobic co-digestion of thermally pretreated scum

2016· article· en· W2323400344 on OpenAlexafffund
Bradley Young, Kevin J. Kennedy, Robert Delatolla, Ranya Sherif

Bibliographic record

VenueWater Quality Research Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMesophileAnaerobic digestionBiogasWaste managementPulp and paper industryChemistryDigestion (alchemy)PopulationBiogas productionActivated sludgeAnaerobic exerciseEnvironmental scienceSewage treatmentMethaneChromatographyBiologyBacteriaMedicineOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The objective of this study is to investigate the mesophilic, anaerobic, co-digestion of scum with thickened waste activated sludge (TWAS) and primary sludge (PS). Observations of the reactor waste show that higher pretreatment temperatures assist in scum integration and appear to limit coagulation and separation effects associated with digesting fats, oils and greases. The 0.40 kg VS/m3 · d scum loaded reactor with scum pretreated at 70 °C achieved the greatest improvement in biogas production over PS and TWAS co-digestion without scum addition. Based on cell viability analysis of the reactor waste, scum-containing reactors were shown to produce 48 and 39% more biogas per m3 of live cells; signifying that scum addition increased the activity of the microbial population embedded in the waste or caused a microbial shift in the waste towards more active communities.

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 score0.002
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)0.0000.000

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 head0.065
GPT teacher head0.344
Teacher spread0.280 · 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
Published2016
Admission routes2
Has abstractyes

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