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Record W2291946720 · doi:10.1080/14680629.2015.1103778

Evaluation of the impact of recycled glass on asphalt mixture performances

2015· article· en· W2291946720 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueRoad Materials and Pavement Design · 2015
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsAsphaltRutMaterials scienceCrackingComposite materialGlass recyclingStripping (fiber)Stiffness

Abstract

fetched live from OpenAlex

The goal of this research was to verify the possibility of using recycled glass particles in an asphalt mixture while maintaining equivalent properties and performance in lieu of a conventional mixture. First, one type of asphalt mixture (ESG14) with different glass contents was tested according to the Ministère des transports du Québec’s mix design method. Next, the performances (resistance to thermal cracking, mixture stiffness and stripping resistance) of an asphalt mixture with optimal glass content were evaluated and compared to a reference mixture. Overall, it was found that using recycled glass in an ESG14 asphalt mixture reduces the binder content, increases the mixture workability and decreases the rutting resistance. It was also found that using 10% recycled glass in an ESG14 asphalt mixture does not impact the resistance to thermal cracking as well as the mixture stiffness. On the other hand, the stripping resistance is negatively affected by the presence of glass.

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)0.003
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.076
GPT teacher head0.307
Teacher spread0.231 · 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