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Record W3199154744 · doi:10.1680/jenge.21.00065

Shear strength of the geomembrane–subgrade interface in heap leaching applications

2021· article· en· W3199154744 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironmental Geotechnics · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsGeomembraneSubgradeGeotechnical engineeringHeap leachingDirect shear testShear (geology)Materials scienceSurface finishCohesion (chemistry)Friction angleGeologyComposite materialMetallurgy

Abstract

fetched live from OpenAlex

A series of large-scale direct shear tests was carried out to examine the effectiveness of smooth, textured and structured surface geomembranes (GMBs) with different soil subgrades for heap leach pad applications. Four different subgrades – namely, sand, two different coarse-grained underliners and a clayey soil representing the layers directly underlying the GMB liner in heap leach pads – were used to examine the shear strength of the GMB–subgrade interfaces at normal stresses between 50 and 1000 kPa. It was found that increasing the normal stresses can change the mechanisms contributing to the shear resistance at the interface. This resulted in a statistically insignificant increase in the interface friction of the GMB–granular soil interfaces when using GMBs with surface roughness relative to the interface friction of the smooth GMB. Furthermore, depending on the type of subgrade, establishing the shear envelopes over a wide range of normal stresses was found to overestimate or underestimate the shear strength at the field stresses even when linear regressions present the best fit for the data.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
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 science0.0000.001
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 head0.006
GPT teacher head0.216
Teacher spread0.210 · 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