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Record W1972174821 · doi:10.1177/0734242x14546036

Enhancing biogas production from anaerobic biodegradation of the organic fraction of municipal solid waste through leachate blending and recirculation

2014· article· en· W1972174821 on OpenAlexaff
Arjun Nair, Majid Sartaj, Kevin J. Kennedy

Bibliographic record

VenueWaste Management & Research The Journal for a Sustainable Circular Economy · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsLeachateBiogasMunicipal solid wasteBioreactorBioreactor landfillWaste managementBiodegradationEnvironmental scienceBiodegradable wasteFraction (chemistry)Biogas productionPulp and paper industryAnaerobic digestionChemistryMethaneEngineeringChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Leachate recirculation has a profound advantage on biodegradation of the organic fraction of municipal solid waste in landfills. Mature leachate from older sections of landfills (>10 years) and young leachate were blended and added to organic fraction of municipal solid waste in a series of biomethane potential assay experiments with different mixing ratios of mature and young leachate and their effect on biogas production was monitored. The improvement in biogas production was in the range of 19%-41% depending on the ratio of mixing old and new leachate. The results are conclusive that the biogas generation could be improved by blending the old and new leachate in a bioreactor landfill system as compared with a conventional system employed in bioreactor landfills today for recirculating the same age leachate.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.001

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 head0.027
GPT teacher head0.289
Teacher spread0.262 · 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 designObservational
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

Citations35
Published2014
Admission routes1
Has abstractyes

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