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Record W2110995818 · doi:10.5276/jswtm.2013.292

Influence of Normal Stress on Hydration of GCLS from Subsoil

2014· article· en· W2110995818 on OpenAlexafffund
Hamid Sarabadani, Mohammad T. Rayhani

Bibliographic record

VenueThe Journal of Solid Waste Technology and Management · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSubsoilStress (linguistics)Environmental scienceSoil science

Abstract

fetched live from OpenAlex

Geosynthetic Clay Liners (GCLs) are often used as part of a barrier system in modern landfills to prevent the escape of leachate into the surrounding environment. The hydraulic performance of GCLs depends on the degree of hydration from the underlying subsoil. The hydration behaviour of two GCL products from different underlying subsoils was examined under various normal stresses (0 to 8 kPa). The rate of hydration of GCLs significantly increased as the normal stress, provided by the waste or leachate collection system, increased from 0 to 8 kPa. For instance, the final equilibrium moisture content of the GCL was achieved in approximately 8 weeks when placed on sand subsoil under 8 kPa normal stress, while it took about 24 weeks for the same GCL under no normal stress. However, the effect of the normal stress on the final equilibrium moisture content of GCLs was not so significant. A normal stress of 2 kPa was shown to induce slightly higher equilibrium moisture uptake for most GCLs. Nevertheless, results indicated a meagre variation of the equilibrium moisture content as the normal stress increased from 2 to 8 kPa. The GCL manufacturing processes and the grain size distribution of the subsoil were shown to affect both the rate of hydration and the final equilibrium moisture content attained.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.203

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 head0.004
GPT teacher head0.200
Teacher spread0.197 · 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 designBench or experimental
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

Citations4
Published2014
Admission routes2
Has abstractyes

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