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Record W3196301239

Numerical modeling of lateral landfill gas migration.

2003· article· en· W3196301239 on OpenAlexaboutno aff
Miroslav Nastev, René Lefebvre, René Therrien, Pierre Gélinas

Bibliographic record

VenueEspaceINRS (National Institute for Scientific Research (Canada)) · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsMethaneLandfill gasCarbon dioxideEnvironmental scienceDecompositionBioreactor landfillEnvironmental engineeringNatural gasPetroleum engineeringWaste managementGeologyChemistryEngineering
DOInot available

Abstract

fetched live from OpenAlex

The decomposition of the organic content of disposed waste results in production of heat and landfill gas, composed mainly of methane and carbon dioxide. The developed pressure, concentration and temperature gradients lead to gas emissions to the atmosphere and to lateral migration through surrounding soils. Environmental and safety issues associated with landfill gas require control of the off-site gas migration. The numerical model TOUGH2-LGM was used to simulate the landfill gas migration through unsaturated sands adjacent to the St-Etienne-des-Grès landfill site in Quebec, Canada, and to assist the design of the off-site gas control system in compliance with regulatory requirements. The model simulates the migration of four fluid components (water, nitrogen, methane and carbon dioxide) and one energy component (heat) in partially saturated media. Two examples are simulated: free lateral gas migration, and lateral gas migration towards a horizontal extraction well. Results show that a vacuum of 0.5 kPa in the horizontal well installed 10m from the landfill at 3 m depth is sufficient to limit further lateral migration of methane. The results also show the different flow patterns for methane and atmospheric air, and demonstrate the advantages of the multicomponent representation of the gas phase.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.053
GPT teacher head0.320
Teacher spread0.267 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations5
Published2003
Admission routes1
Has abstractyes

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