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Record W1988832641 · doi:10.3141/2444-12

Effect of Waste Engine Oil Residue on Quality and Durability of SHRP Materials Reference Library Binders

2014· article· en· W1988832641 on OpenAlexafffundabout
Kelli-Anne N. Johnson, Simon A.M. Hesp

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2014
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDurabilityMaterials scienceAsphaltCrackingDynamic shear rheometerComposite materialWaste managementEnvironmental scienceRutEngineering

Abstract

fetched live from OpenAlex

This paper discusses the effects of widely used waste engine oil (WEO) residue on asphalt binder properties. Five SHRP Materials Reference Library binders and one commercial material from Ontario, Canada, were modified with 15% WEO. Materials were aged according to a standard rolling thin film oven protocol followed by 20 and 40 h of pressure aging vessel (PAV). Binder quality was determined according to AASHTO specification M 320 as well as dynamic shear characterization. Durability was assessed after additional conditioning at high temperature in the PAV and at low temperature in the bending beam rheometer. All but one of the WEO-modified materials transitioned to a gel-type state more readily upon chemical and physical conditioning; this result explains the poor thermal cracking performance seen for such binders on Ontario roads.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.077
GPT teacher head0.377
Teacher spread0.301 · 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 designBench or experimental
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

Citations71
Published2014
Admission routes3
Has abstractyes

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