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Record W1996185379 · doi:10.3141/2306-06

Ten-Year Performance of Full-Depth Reclamation with Expanded Asphalt Stabilization on Trans-Canada Highway, Ontario, Canada

2012· article· en· W1996185379 on OpenAlexaffabout
Becca Lane, Tom Kazmierowski

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2012
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsLand reclamationAsphaltChristian ministryOverlayAggregate (composite)Civil engineeringAsphalt pavementEnvironmental scienceEngineeringGeotechnical engineeringForensic engineeringGeographyComputer scienceMaterials scienceArchaeology

Abstract

fetched live from OpenAlex

In 2001, the Ministry of Transportation Ontario, Canada, constructed its first stabilization project involving full-depth reclamation with expanded (foamed) asphalt on the Trans-Canada Highway, south of Wawa, Ontario. The project involved three mix designs, two with corrective aggregate and one without corrective aggregate. A control section of full-depth reclamation with the same thickness of hot-mix overlay (without expanded asphalt stabilization) was placed in the middle of the project. The project has been monitored annually for the past 10 years. Analysis of roughness data and pavement distress data indicated a significant difference between the test sections with expanded asphalt stabilized base and the control section. The expanded asphalt stabilization has delivered superior performance compared with the conventional full-depth reclamation with hot-mix overlay. Performance curves for the treatments on this project were compared with the ministry's average performance curve for full-depth reclamation (reconstruction) projects and with the performance of treatments on two adjacent projects. This project demonstrated the exceptional performance of the expanded asphalt mixes through 10 years of proven superior pavement condition and ride.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.052
GPT teacher head0.301
Teacher spread0.249 · 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 designObservational
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

Citations10
Published2012
Admission routes2
Has abstractyes

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