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Long-Term Numerical Simulation of Methane Transport and Oxidation in Compost Biofilter

2009· article· en· W2056075495 on OpenAlexaff
Lei Yuan, Tarek Abichou, Jeffrey P. Chanton, David K. Powelson, Alex De Visscher

Bibliographic record

VenuePractice Periodical of Hazardous Toxic and Radioactive Waste Management · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiofilterCompostMethaneAnaerobic oxidation of methaneMethanotrophChemistryLandfill gasEnvironmental scienceDiffusionPorosityEnvironmental chemistryEnvironmental engineeringSoil scienceChemical engineeringWaste managementThermodynamicsEngineering

Abstract

fetched live from OpenAlex

A 30-cm-thick biofilter was constructed with compost, which was obtained from Leon County Landfill (Florida). The compost was sieved with 7-mm mesh sieve. The compost consisted of chipped yard waste that was windrowed for about 5years. Methane was then continuously supplied to the bottom of the biofilter, simultaneously the outflow of methane from the top and the extent of methane oxidation inside the biofilter were periodically measured. A one-dimensional dynamic numerical simulation model was then developed to simulate the methane transport and oxidation within a compost biofilter. This model was designed to incorporate dynamic parameters, such as gas permeability, diffusion coefficient, methanotrophic growth, and viscosity, as functions of water content and temperature. General agreement of methane outflux and oxidation was obtained between model simulations and experimental data. Additional simulations showed that outflux and oxidation had high correlations with temperature, whereas their relationship with water content depended on other factors, such as the influx boundary and the air-filled porosity.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.268
Teacher spread0.258 · 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 designSimulation or modeling
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

Citations29
Published2009
Admission routes1
Has abstractyes

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