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Record W2772383042 · doi:10.1680/jgein.17.00027

Gas advection-diffusion in geosynthetic clay liners with powder and granular bentonites

2017· article· en· W2772383042 on OpenAlexaff
Abdelmalek Bouazza, M. A. Rouf, Rao Martand Singh, R. Kerry Rowe, Will P. Gates

Bibliographic record

VenueGeosynthetics International · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
FundersAustralian Research Council
KeywordsGeosynthetic clay linerGravimetric analysisPermeability (electromagnetism)BentoniteSaturation (graph theory)Gaseous diffusionPore water pressureGeotechnical engineeringDiffusionOverburden pressureMaterials scienceAdvectionMineralogyComposite materialGeologySoil scienceChemistrySoil waterHydraulic conductivityThermodynamicsMembrane

Abstract

fetched live from OpenAlex

Gas diffusion and gas permeability tests were performed sequentially on powder and granular partially hydrated needle-punched geosynthetic clay liners (GCLs) over a range of gravimetric water content using a gas flow unified measurement system under 2 kPa and 20 kPa vertical stresses. Most of the changes in diffusion and advection occurred at intermediary levels of saturation or gravimetric water contents where diffusive and advective gas migration in the granular GCL tended to be higher than in the powder GCL. When the GCLs were relatively dry, their gas diffusion and gas permeability remained constant due to the large interconnected air voids present in the bentonites. For relatively wet conditions, the difference in their gas diffusion and gas permeability was minimal as the bentonites developed a relatively uniform gel structure. The results suggest that at a nominal overburden pressure of 20 kPa, GCLs such as the ones studied need to be hydrated to more than 160% gravimetric water content or >80% apparent degree of saturation before gas diffusion and permeability drop to 1.0 × 10 −11 m 2 /s and 2 × 10 −13 m/s, respectively.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.999

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 studies0.0000.001
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 head0.007
GPT teacher head0.234
Teacher spread0.226 · 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 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

Citations33
Published2017
Admission routes1
Has abstractyes

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