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Record W1974722821 · doi:10.1680/gein.2011.18.2.63

Antioxidant depletion in high-density polyethylene pipes exposed to synthetic leachate and air

2011· article· en· W1974722821 on OpenAlexafffund
R.P. Krushelnitzky, R.W.I. Brachman

Bibliographic record

VenueGeosynthetics International · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLeachateAntioxidantPolyethyleneArrhenius equationChemistryMoistureWaste managementEnvironmental engineeringMaterials scienceEnvironmental scienceComposite materialEnvironmental chemistryOrganic chemistryActivation energyEngineering

Abstract

fetched live from OpenAlex

ABSTRACT: Antioxidant depletion rates are reported for samples of one particular high-density polyethylene pipe when immersed in air and a synthetic municipal solid waste leachate, obtained by measuring the oxidative induction time (OIT) at temperatures of 22°C, 40°C, 70°C and 85°C with time. Of the factors examined, the rate of antioxidant depletion was affected most by the ageing temperature, with much faster depletion occurring at higher temperatures. Antioxidant depletion was faster when immersed in the synthetic leachate rather than in air, and faster for the thin pipe examined compared with a thick pipe. No significant difference in antioxidant depletion was found whether the pipe was deflected with a 10% reduction in vertical outside pipe diameter or not deflected. Predictions of the time to deplete antioxidants are then made from Arrhenius extrapolations of the reported data. It is estimated that depletion of antioxidants may take from as long as 600 years at 10°C to as little as 20 years at 50°C when exposed to air, and may reduce to 160 years at 10°C or 10 years at 50°C if exposed to the synthetic leachate. These results are applicable for the particular pipe, antioxidant formulation and conditions examined.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.487

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 head0.016
GPT teacher head0.218
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

Citations9
Published2011
Admission routes2
Has abstractyes

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