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Record W577944826

Preliminary Findings from Saskatchewan's Asphalt Rubber Project

2008· article· en· W577944826 on OpenAlexaboutno aff
Am Anthony, CF Berthelot

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltNatural rubberCrumb rubberAsphalt pavementChristian ministryAsphalt concreteEnvironmental scienceRutGeotechnical engineeringCivil engineeringForensic engineeringEngineeringMaterials scienceComposite material
DOInot available

Abstract

fetched live from OpenAlex

Use of crumb rubber as an additive in asphalt concrete mixes has been predominantly limited to milder climates and the performance of asphalt rubber pavements in Canada has been variable. Saskatchewan Ministry of Highways and Infrastructure (MHI) was designed and constructed an asphalt rubber pavement on Highway 11, near Findlater, as part of a rehabilitation strategy for this structural asphalt concrete pavement highway. Test sections were constructed in July 2005 to evaluate the effectiveness of the rubber asphalt mix as a rehabilitation overlay treatment for typical Saskatchewan asphalt concrete pavements, in comparison to the dense-graded asphalt mixes used by MHI. Laboratory testing was carried out to complement the field evaluation, and to quantify the mechanistic properties of the asphalt rubber mix. As well, structural field testing was performed to evaluate in-situ properties of the asphalt rubber and the conventional densegraded asphalt mix. To date both the conventional mix and the asphalt rubber are performing well in the field. Based on the laboratory characterization results, minimal difference in material properties was observed between the conventional asphalt mix and asphalt rubber mix at 20oC. Additionally, the non-destructive structural characterization results indicate that to date the conventional mix and the asphalt rubber are performing comparably in terms of deflections.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score1.000

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)0.0010.001

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 head0.025
GPT teacher head0.241
Teacher spread0.216 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations2
Published2008
Admission routes1
Has abstractyes

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