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Record W4413369882 · doi:10.1051/geotech/2025020

Gas capture and extraction using multi-linear drainage géocomposite

2025· article· en· W4413369882 on OpenAlexaff
Hajer Bannour, David Beaumier, Stéphan Fourmont

Bibliographic record

VenueRevue Française de Géotechnique · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsCTT Group (Canada)
Fundersnot available
KeywordsExtraction (chemistry)DrainageEnvironmental scienceHydrology (agriculture)Computer scienceGeologyChromatographyChemistryGeotechnical engineeringBiologyEcology

Abstract

fetched live from OpenAlex

The design of gas drainage systems is essential for mitigating environmental impacts, especially in contaminated soils (e.g., hydrocarbons, radon) and landfill covers (e.g., methane and carbon dioxide management). This study experimentally investigates the gas drainage capacity and transport through mini pipes within multi-linear drainage geocomposites, as these mini pipes primarily determine the system’s overall drainage capacity. Initially, the assessment of the discharge capacities was performed on air, and CO₂ through various mini-pipe diameters and lengths to characterize the flow rate as a function of the gradient. Finally, the drainage capacity through mini-tube connections and their integration into the principal collection system was examined. These two steps enable the estimation of both linear and singular head losses, considering the extended lengths of the mini-pipes and their connections. The final objective is to extend these findings to gases such as methane and radon by establishing discharge equivalencies among various fluids. This experimentation is supported by well-known fluid transport concepts, allowing for the modeling and reproduction of the drained flow rate as a function of the fluid gradient. This paper presents the methodology and results of the proposed study, along with various analyses of equivalency considerations to enhance gas drainage system design.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.664
Threshold uncertainty score0.997

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 science0.0000.000
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 head0.019
GPT teacher head0.258
Teacher spread0.239 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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