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Record W4322013251 · doi:10.1061/jggefk.gteng-11101

Degradation Behavior of Two Multilayered Textured White HDPE Geomembranes and Their Smooth Edges

2023· article· en· W4322013251 on OpenAlexaff
M. Zafari, F.B. Abdelaal, R. Kerry Rowe

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsGeomembraneMaterials scienceComposite materialEnhanced Data Rates for GSM EvolutionHigh-density polyethyleneUltimate tensile strengthCore (optical fiber)Degradation (telecommunications)LeachateGeotechnical engineeringPolyethyleneWaste managementGeology

Abstract

fetched live from OpenAlex

The longevity of two white multilayered textured geomembranes, manufactured with a smooth edge for welding, is examined when immersed in synthetic municipal solid waste leachate at five temperatures (40, 55, 65, 75, and 85°C) for 50 months. The textured and smooth parts of the geomembranes had similar initial chemical and physical properties, but the core of the smooth edge was thinner. It is shown that, depending on the relative effects of the difference in the core thickness and the surface area exposed to a solution, antioxidant depletion of the textured portion may be either faster or slower than the smooth edge. However, due to the lesser core thickness of the smooth edge of both geomembranes, the degradation in the tensile stress at break properties at 85°C is faster for the smooth edge than for the textured portion of the roll even when the antioxidant depletion was faster for the textured part. Thus, any assessment of the likely service life of the textured geomembrane requires consideration of both the smooth and textured portions of a multilayered textured geomembrane.

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 score0.880
Threshold uncertainty score0.684

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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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