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Record W4206361972 · doi:10.14195/2184-8394_110_1

Advances and remaining challenges for geosynthetics in geoenvironmental engineering applications

2007· article· en· W4206361972 on OpenAlexafffund
R. Kerry Rowe

Bibliographic record

VenueGeotecnia · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeosynthetic clay linerGeosyntheticsGeomembraneLeachateHydraulic conductivityGeotechnical engineeringComposite numberEnvironmental scienceMaterials scienceEngineeringWaste managementComposite materialSoil water

Abstract

fetched live from OpenAlex

Nine issues of importance to the use of geomembranes (GMs) and geosynthetic clay liners (GCLs) as part of composite liners in geoenvironmental applications are examined. These issues include the effect of: GCL-leachate compatibility on hydraulic conductivity; freeze-thaw on GCL performance; internal erosion on GCL hydraulic conductivity; temperature on advection and diffusion as well as desiccation of GCLs and compacted clay liners (CCLs); the choice of protection layer on the strains developed in GMs; wrinkles on strains developed in GMs and the thinning of GCLs; holes in GMs on leakage through composite liners; winkles in GMs on leakage through composite liners; diffusion through GCLs and GMs; and temperature and leachate exposure on the service life of GMs. It is suggested that GCLs and GMs can play a very beneficial role in providing environmental protection. However, like all engineering materials they must be used appropriately and consideration should be given to factors such as those addressed in this paper. There is a need for site specific design, strict adherence to construction specification, and appropriate protection of the geosynthetics after construction. In particular, given the diversity of available GCLs and their different engineering characteristics, GCLs should be selected based on the required engineering properties, not just price.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.531

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 head0.007
GPT teacher head0.221
Teacher spread0.213 · 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 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

Citations3
Published2007
Admission routes2
Has abstractyes

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