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Record W1851112144 · doi:10.1139/t10-055

A method to assess risk reduction when utilizing geosynthetic clay liners (GCLs) with compacted soil liners

2010· article· en· W1851112144 on OpenAlexafffundvenue
W. Todd Menzies, Gordon A. Fenton, Craig B. Lake, D. V. Griffiths

Bibliographic record

VenueCanadian Geotechnical Journal · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDartmouth College
KeywordsHydraulic conductivityGeosynthetic clay linerGeotechnical engineeringGeosyntheticsEnvironmental scienceGeologySoil scienceSoil water

Abstract

fetched live from OpenAlex

This paper presents an analytical solution developed to estimate probabilities of “failure” or advective flux “exceedance” for the case of a spatially variable geosynthetic clay liner (GCL) situated over a spatially variable compacted soil liner (CSL). The risk of utilizing such a liner system is assessed relative to a regulatory compacted clay-based soil liner. The analytical solution developed is validated over a range of parameters against random field simulation using the Local Average Subdivision method, and the analytical solution is shown to be in good agreement with simulation. The analytical solution is then used to examine the “probability of exceedance” for a spatially variable GCL and CSL combined liner system. It is shown that the use of a GCL can potentially result in a low probability of exceedance when used with a spatially varying, high hydraulic conductivity CSL. The risk of exceedance generally decreases as the hydraulic conductivity of the CSL decreases. An example problem is presented to demonstrate the capabilities of the analytical solution.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.267
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations7
Published2010
Admission routes3
Has abstractyes

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