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Record W2000127323 · doi:10.1080/14680629.2008.9690158

Glass Transition and Phase Stability in Asphalt Binders

2008· article· en· W2000127323 on OpenAlexafffund
Pavel Kříž, Jiri Stastna, Ludo Zanzotto

Bibliographic record

VenueRoad Materials and Pavement Design · 2008
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsGlass transitionDifferential scanning calorimetryAsphaltMaterials scienceAmorphous solidIsothermal processPhase transitionComposite materialDiscontinuity (linguistics)PolymerPhase (matter)ThermodynamicsChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Major aspects of the glass transition of asphalt binders are described and an extensive literature review of the phenomenon and its relation to chemical composition is presented. The glass transition of asphalt binders was studied by modulated differential scanning calorimetry and also via dynamic mechanical analysis. A certain analogy between the glass transition of amorphous polymers and asphalts is suggested. The overall transition was found to be very broad on the temperature scale. The effects of evaporation of light-end components and oxidation on asphalt phase stability and glass transition were studied. It was suggested that phase incompatibility may exist in asphalts; however, the phase separation is observable after long-term isothermal conditioning at a temperature within the glass transition range. Based on the presented results, it is suggested that phase incompatibility develops if there is a discontinuity in the molecular distribution. Such discontinuity may be present in some neat binders as well as in severely oxidized or aged asphalt binders.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.259
Teacher spread0.204 · 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 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

Citations107
Published2008
Admission routes2
Has abstractyes

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