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Record W591557282

Improving Canadian Performance of SMA by Maximizing Best Practices

2005· article· en· W591557282 on OpenAlexaboutno aff
JA Scherocman, Sl Tighe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltDurabilityGradationEngineeringSMA*Context (archaeology)Aggregate (composite)CompactionForensic engineeringCivil engineeringComputer scienceGeotechnical engineeringMaterials science
DOInot available

Abstract

fetched live from OpenAlex

Stone Mastic Asphalt (SMA) is a durable, rut resistant mix that relies on a stone-on-stone contact to provide strength while utilizing a rich mortar binder to provide durability. It has been used in Canada since 1990 and in the United States since 1991. Although it provides many technical and economic benefits, SMA requires detailed attention during the production and placement process. The stone-on-stone contact which provides the durability of the mix can be achieved through proper gradation and the use of hard durable aggregate. In addition, the mix must be properly designed in terms of the asphalt content, the associated air void content, and the appropriate design for voids in the mineral aggregate must be selected. Finally, the mix must meet moisture susceptibility and draindown requirements. In order to achieve a good product in the field, special attention must be given to ensure good production and construction practices. At the 2004 Canadian Technical Asphalt Association Contractor's Workshop, which focused on SMA pavements, it was evident that contractors, consultants, and transportation agencies are struggling to achieve quality with these pavements. Various problems had been identified ranging from mix design issues related to the use of fibers, to plant production problems associated with the addition of fines, to compaction issues, and draindown in the field. This paper is thus directed at providing state of the art practice in SMA pavements in the Canadian context.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.994

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 communication0.0000.001
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 head0.024
GPT teacher head0.250
Teacher spread0.225 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2005
Admission routes1
Has abstractyes

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