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Record W608780744

Overview of the Design of Emulsion Based Seal Coating Systems Available in Canada and Abroad

2005· article· en· W608780744 on OpenAlexaboutno aff
J K Davidson, Peter Linton, JM Croteau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsSeal (emblem)CoatingImpervious surfaceLayer (electronics)EmulsionAsphaltMaterials scienceComputer scienceEngineeringEnvironmental scienceComposite material
DOInot available

Abstract

fetched live from OpenAlex

Seal coat systems have been used in Canada and abraod for many decades. Seal coats are thin wearing courses made of superimposed layers of aggregate and bituminous binder. They may be used to restore the surface characteristics of existing roadways or to waterproof and preserve others. Seal coats form an impervious thin overlay over an existing bound or unbound material. There are two families of treatments: the chip seal and graded seal. Chip seals combine the application of a layer of calibrated chips onto a layer of rapir setting emulsion while the graded seals are systems that combine the application of dense/gap graded aggregate onto a layer of anionic high float emulsion. Each system may be applied as a single or as a multiple application. Parameters such as the traffic and the existing surface conditions must be taken into account in the design of a specific seal coat system for a given roadway. Field conditions such as ambient temperature, the time of the year, the sun/cloud conditions must be taken into account as well. This paper presents the seal coating technologies and a discussion on the state of the design practices of these surface treatments in Canada and abroad. The paper introduces new concepts related to the selection of seal coating systems, as well as emerging systems now available in North America.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.787

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)0.0000.000

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 head0.035
GPT teacher head0.245
Teacher spread0.210 · 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

Citations4
Published2005
Admission routes1
Has abstractyes

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