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Record W587643887 · doi:10.33593/iccp.v5i1.827

PERFORMANCE OF ONTARIO'S FIRST MAJOR REHABILITATION AND OVERLAY PROJECT

2025· article· en· W587643887 on OpenAlexaboutno aff
Tom Kazmierowski, H Sturm

Bibliographic record

VenueProceedings of the International Conference on Concrete Pavements · 2025
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsOverlayChristian ministryEngineeringDriver rehabilitationRehabilitationTransport engineeringForensic engineeringGrindingCivil engineeringDiamond grindingComputer scienceMechanical engineeringGrinding wheel

Abstract

fetched live from OpenAlex

The rehabilitation of concrete freeways has challenged highway authorities for many years. This challenge has been met with the utilization of various techniques such as full and partial depth repairs, diamond grinding and unbonded overlays. In 1989, the Ontario Ministry of Transportation constructed a project to demonstrate the feasibility of these techniques in their jurisdiction, and to monitor the long term performance of the various applications. The project incorporated the use of full depth and partial depth repairs followed by diamond grinding and joint sealant replacement on the northbound lanes (NBL). The southbound lanes (SBL) received an 180 mm thick plain jointed unbonded PCC (JPCP) overlay. The rehabilitation project has now been in service for three years and a number of performance characteristics have been monitored. These include pavement roughness and skid resistance with testing completed on an annual basis while pavement load response, condition surveys and noise emissions testing have been undertaken on an as need basis. The results of this testing and analysis are presented in this paper.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.001
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.010
GPT teacher head0.228
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the International Conference on Concrete PavementsSame topicInfrastructure Maintenance and MonitoringFrench-language works237,207