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Record W3200345819 · doi:10.1680/jenge.20.00027

Performance of food-waste compost biocovers in mitigating methane emission from landfills

2021· article· en· W3200345819 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.

Bibliographic record

VenueEnvironmental Geotechnics · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsCompostMethaneFood wasteGreen wasteMunicipal solid wasteEnvironmental scienceLandfill gasWaste managementEnvironmental chemistryPulp and paper industryEnvironmental engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

This paper presents the results of an experimental programme that was employed to investigate the performance of biocovers made of food-waste compost in mitigating methane emissions from municipal solid waste landfills in a semi-dry environment. Five experimental columns containing biocover materials made of compost mixed with landfill intermediate cover soil at different compost/soil mixture ratios were exposed to methane inflow under ambient temperature over a period of 3 months. Methane removal efficiencies were determined based on methane content measurements using gas chromatography, bacterial count and scanning electron microscopy performed on biocover samples over time. The biocover materials made of 70% compost and 30% soil demonstrated significantly higher methane removal efficiencies compared with other mixtures, measuring an emission reduction of about 63%. The compost type and composition were also found to affect the methane removal efficiency of biocover materials. These findings can be used for selection of compost type and compost/soil mixture ratio as biocover materials.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.144
Threshold uncertainty score1.000

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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