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Record W3193617784 · doi:10.1139/cgj-2021-0222

Design guidance for protection of geomembrane liners in landfill applications

2021· article· en· W3193617784 on OpenAlexafffundvenue
Bryce Marcotte, Ian Fleming

Bibliographic record

VenueCanadian Geotechnical Journal · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeomembraneCushioningGeotextileGeotechnical engineeringSiltGeosyntheticsGeosynthetic clay linerStiffnessEnvironmental scienceMaterials scienceComposite materialSoil waterGeologySoil scienceHydraulic conductivity

Abstract

fetched live from OpenAlex

A new method of evaluating strains and predicting required protection layers that are placed over geomembranes is developed based on the combined effects of the clay strength and stiffness and the cushioning effect of a nonwoven protection layer. Plots giving the required geotextile protection for different maximum strains are presented for expected landfill pressures under 300 kPa for angular, 38 mm gravel placed above a geomembrane liner for both drained and undrained loading conditions of the clay. A similar plot is also given for tire-derived aggregate placed above the liner for pressures under 500 kPa for undrained loading conditions. All tests were conducted at room temperature. For all cases, nonwoven geosynthetic protection layers with mass per unit area (MUA) exceeding at least 1200 g/m2 were required to limit long-term strains below current threshold levels. The MUA of the protection layer, for the particular aggregates and geomembranes tested, is dependent on the loading rate, water content relative to optimum, the silt content, and the activity of the clay below.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.022
GPT teacher head0.237
Teacher spread0.215 · 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 designTheoretical or conceptual
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

Citations11
Published2021
Admission routes3
Has abstractyes

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Same venueCanadian Geotechnical JournalSame topicLandfill Environmental Impact StudiesFrench-language works237,207