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Record W371020619

Long Term Pavement Performance of an Aging Open Graded Friction Course Asphalt Surface

2009· article· en· W371020619 on OpenAlexaboutno aff
J Laxdal, M Oliver, M. Bishop, D. Finlayson, Clair Wakefield, P Marks, C Cautillo, Kk Tam, S Mcinnis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsGradationAsphaltSkid (aerodynamics)Wearing courseRutGeotechnical engineeringEngineeringTraffic noiseChristian ministryEnvironmental scienceForensic engineeringCivil engineeringMaterials scienceStructural engineeringNoise reductionComposite material
DOInot available

Abstract

fetched live from OpenAlex

This paper describes a long term follow-up study carried out by the British Columbia Ministry of Transportation and Infrastructure on two paving projects constructed in the mid 1990's using Open Graded Friction Course (OGFC) asphalt pavement. OGFC pavements have a much coarser gradation, significantly higher air voids, and greater asphalt film thickness than conventional asphalt pavements. Benefits include reduced ambient traffic noise, improved safety due to increased skid resistance, improved surface drainage, reduced glare, and reduced spray during wet conditions. The study focuses on two projects constructed in and near Nanaimo, British Columbia in 1995, 1996 and 1997. One was constructed in two phases on Highway 19A in the City of Nanaimo. The other project was constructed on Highway 19 built to service the Duke Point Ferry Terminal. The paper presents recent and past data regarding these pavement characteristics, and analysis of the long term performance of the pavement in terms of traffic noise attenuation, skid resistance, pavement roughness, pavement condition, rut depth, remaining pavement life, and permeability.

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 score0.252
Threshold uncertainty score0.688

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 head0.025
GPT teacher head0.292
Teacher spread0.267 · 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
Published2009
Admission routes1
Has abstractyes

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