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Durability of Three HDPE Geomembranes Immersed in Different Fluids at 85°C

2014· article· en· W1971129974 on OpenAlexaffabout
F.B. Abdelaal, R. Kerry Rowe

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsLeachateGeomembraneHigh-density polyethyleneDegradation (telecommunications)AntioxidantEnvironmental chemistryChemistryPolyethyleneMunicipal solid wasteWaste managementEnvironmental scienceMaterials scienceComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

The long-term performance of three different high-density polyethylene (HDPE) geomembranes (GMBs) is investigated at 85°C using immersion tests. By comparing the degradation behavior of the three GMBs in different synthetic leachates, it is shown that different chemical constituents in the leachate affected different stages of the degradation, with surfactant having the greatest effect on antioxidant depletion (Stage I) and salts having the greatest effect on the degradation after antioxidant depletion (Stages II and III). The magnitude of the effect of these chemical constituents differed from one GMB to another. Thus, for the purpose of comparing the relative long-term performance of the three GMBs for municipal solid waste (MSW) landfill applications, the GMBs were immersed in a synthetic leachate (Leachate A), which contained the primary constituents (i.e., salts, volatile fatty acids, surfactant, and trace metals under reduced conditions) present in the leachate from a large MSW landfill leachate located in Canada. At 85°C, the longest antioxidant depletion stage was for the GMB with the highest resistance to antioxidant depletion in Leachate A, even though its initial oxidative induction time values were not the highest of the three GMBs. After antioxidant depletion, the greatest resistance to degradation was for the GMB with the highest initial stress crack resistance and the lowest melt flow ratio.

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 score0.683
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.004
GPT teacher head0.170
Teacher spread0.166 · 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

Citations21
Published2014
Admission routes2
Has abstractyes

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