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Record W2613832680 · doi:10.1139/cgj-2017-0049

Long-term performance of high-density polyethylene (HDPE) geomembrane seams in municipal solid waste (MSW) leachate

2017· article· en· W2613832680 on OpenAlexafffundvenue
R. Kerry Rowe, Mohammad Shoaib

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
FundersCanadian Nuclear Safety CommissionKillam TrustsMinistry of EnvironmentNatural Sciences and Engineering Research Council of CanadaQueen's University
KeywordsGeomembraneLeachateHigh-density polyethyleneWeldingShear (geology)PolyethyleneMaterials scienceMunicipal solid wasteEnvironmental scienceGeotechnical engineeringMetallurgyComposite materialWaste managementGeology

Abstract

fetched live from OpenAlex

The effect of a synthetic municipal solid waste leachate on the long-term performance of dual-wedge welds in a 1.5 mm thick high-density polyethylene geomembrane (GMB) is reported based on 4 years of testing at 40, 65, 75, and 85 °C. The effect of leachate on the GMB well away from the weld, in the heat-affected zone (HAZ) beside the weld, and in the welded zone are investigated. The slowest antioxidant depletion rate was in the weld itself and the fastest rate for the HAZ adjacent to the weld. The shear break and peel break properties started to decrease after the standard oxidative induction time had depleted to residual, but before the high-pressure oxidative induction time had reached residual. Failures occured at the HAZ adjacent to weld in both the shear and peel tests. No failure of the seam itself was observed. The times to nominal failure of the GMB in the critical HAZ are predicted. The rate of degradation in the weld and sheet are compared.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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 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

Citations38
Published2017
Admission routes3
Has abstractyes

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