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Record W2010693135 · doi:10.1680/gein.13.00019

Prediction of geosynthetic clay liner desiccation in low stress applications

2013· article· en· W2010693135 on OpenAlexafffund
R. Kerry Rowe, Ashley Verge

Bibliographic record

VenueGeosynthetics International · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaResearch and Innovation Foundation
KeywordsDesiccationGeosynthetic clay linerGeotechnical engineeringGeosyntheticsMaterials scienceEnvironmental scienceInfiltration (HVAC)Saturation (graph theory)Soil scienceComposite materialGeologyEcologySoil waterMathematicsHydraulic conductivityBiology

Abstract

fetched live from OpenAlex

ABSTRACT: The risk of geosynthetic clay liner (GCL) desiccation in low stress applications such as solar ponds was investigated using a fully coupled thermo-hydro-mechanical model. Parameters were developed and verified by comparison with previously reported laboratory data. A parametric study was performed to establish recommendations for future investigation. The water retention curve of the GCL was found to have a significant effect on the conditions that are expected to cause desiccation. The temperature coefficient of the water retention curve was also found to have a significant effect, yet this parameter is not well defined. Poisson's ratio was found to affect the risk of desiccation in proportion to the applied stress. As reported by previous researchers, the initial degree of saturation of a GCL was found to influence desiccation; however, the effect was diminished at low applied stress. The magnitude of the increase in liner temperature, above groundwater temperature, was found to have the potential to cause desiccation of a GCL in a composite liner.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

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

Citations22
Published2013
Admission routes2
Has abstractyes

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