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Record W2948001002 · doi:10.1515/arh-2000-0012

Thermomechanical Properties of Several Polymer Modified Asphalts

2000· article· en· W2948001002 on OpenAlexafffund
Ludo Zanzotto, Jiri Stastna, Otakar Vacin

Bibliographic record

VenueApplied Rheology · 2000
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAsphaltRheologyViscoelasticityMaterials scienceComposite materialPolymerAsphalt pavement

Abstract

fetched live from OpenAlex

Abstract Costly deterioration of many roads and highways paved with asphalt generates a growing interest in polymer modified asphalts (PMA). Although asphalt represents only a fraction of asphalt paving mix, it is believed that it has a significant effect on the thermomechanical properties of asphalt pavements. Currently there is no consensus on the type of tests and specifications for polymer modified asphalts; however, it is clear that such specifications should be based on rheological testing of these systems. As a viscoelastic material asphalt is usually characterized by its dynamic material functions. An advantage of the use of modified loss tangent function in PMA systems is discussed in this contribution. Three polymer modifiers (SBS, EVA and EGA) commonly used in the asphalt paving industry were blended with base asphalt and various relaxations in the base, and its blends are studied here.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.200
Teacher spread0.185 · 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

Citations45
Published2000
Admission routes2
Has abstractyes

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