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Record W2149713265 · doi:10.1139/l07-092

Asphalt modification with used lubricating oil

2008· article· en· W2149713265 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAsphaltRheometerMaterials scienceComposite materialDynamic shear rheometerCrackingRutRheology

Abstract

fetched live from OpenAlex

The viability of used lubricating oil as an asphalt modifier was studied, with the enhancement of the low-temperature grade as the specific goal. Used oil modification was found to improve the Superpave low-temperature performance grade (PG), but at the expense of the high-temperature PG grade. When evaluated according to the Superpave MP1 specification, the low-temperature grade of the modified asphalt was not significantly improved due to failure of the bending beam rheometer (BBR) test’s m value. When evaluated according to the Superpave MP1a specification, the modified asphalt overall PG grade temperature spread remained essentially constant, varying only by approximately two degrees. The asphalt took as much as 12% of oil and still had less than the maximum limit of 1.0% rolling thin film oven test (RTFOT) mass loss (emissions). However, the oil may possibly have a detrimental effect on the asphalt quality, such as reduced adhesiveness to the aggregates, leading to stripping and raveling. The field performance test should be checked before considering lubricating oil as a modifier.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.573

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 head0.022
GPT teacher head0.201
Teacher spread0.179 · 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