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Record W4387019762 · doi:10.1680/jgein.23.00096

Assessment of the stress crack resistance of multilayered textured HDPE geomembranes

2023· article· en· W4387019762 on OpenAlexaff
F.B. Abdelaal, M. Zafari, R. Kerry Rowe

Bibliographic record

VenueGeosynthetics International · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsGeomembraneMaterials scienceEnhanced Data Rates for GSM EvolutionGeosyntheticsComposite materialStress (linguistics)LeachateGeotechnical engineeringWaste managementGeology

Abstract

fetched live from OpenAlex

The stress crack resistance (SCR) of different multilayered textured geomembranes (GMBs) immersed in synthetic municipal solid waste leachate at 55°C and 85°C for 50 months was assessed using their smooth edge or smooth equivalent GMBs made from the same formulation as textured GMBs. The difference between the actual thickness of the smooth edge and the nominal thickness of the GMB was shown to result in SCR values that do not reflect the true SCR of the GMB resin when assessed based on the current ASTM standard test procedure. As such, adjustments to the current test method are presented to limit its effect on SCR, especially when comparing the performance of different GMBs based on SCR. Additionally, due to variations in formulation and thickness of the textured and smooth portions of GMB rolls, a new double-notching technique was examined to assess the SCR of the textured portion. Using this technique on samples from textured and smooth portions at different incubation times showed similar initial and stabilised SCR values but different SCR degradation rates for the two different parts of the same GMB roll. Relying solely on SCR values obtained from the smooth edge/smooth equivalent GMB may thus underestimate or overestimate the degradation of multilayered textured GMBs.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score1.000

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 science0.0010.001
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 head0.010
GPT teacher head0.267
Teacher spread0.257 · 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.

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

Citations6
Published2023
Admission routes1
Has abstractyes

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