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Record W650932098

The Benefits of Modified Asphalts

2014· article· de· W650932098 on OpenAlexaboutno aff
Dwight Walker

Bibliographic record

VenueAsphalt · 2014
Typearticle
Languagede
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltRutService lifeDurabilityOverlayCrumb rubberCrackingElastomerAsphalt pavementForensic engineeringFoundation (evidence)Environmental scienceEngineeringMaterials scienceComposite materialComputer science
DOInot available

Abstract

fetched live from OpenAlex

This article reviews the benefits of modified, high performance asphalts, notably improved rutting resistance, with less thermal (cold temperature) cracking and overall improved mixture durability. The author notes that many agencies estimate that they can get an additional four to six years of pavement life from a pavement constructed using a modified asphalt binder. The article briefly reviews the history of strategies to lengthen pavement service life, then discusses the different types of modifiers used, including elastomers (rubbers or elastics), and plastomers (plastics), which are two types of the more-generic term, polymers. One common elastomer is crumb rubber, which is made from ground tires. The author also reports on a study undertaken by the Asphalt Institute to quantify the performance benefits of polymer-modified asphalt pavements and overlaps using field data from across the U.S. and Canada. The article concludes with a detailed table that provides information about site features, conditions, and the expected increase in service life when using flexible pavements and hot mix asphalt overlays; site features include foundation soils, water table depth, traffic volume, climate, and existing pavement condition.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.020
GPT teacher head0.243
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2014
Admission routes1
Has abstractyes

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