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Record W63246890

Engineering Tools and Standards Applied in Preserving Ontario’s Provincial Highways

2008· article· en· W63246890 on OpenAlexaboutno aff
Ningyuan Li, Tom Kazmierowski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsAsset managementTransport engineeringInvestment (military)Pavement managementAsset (computer security)IT asset managementBusinessEngineeringEngineering managementComputer scienceFinanceComputer security
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the road asset management concepts, engineering standards and technical tools developed by Ministry of Transportation of Ontario (MTO) for preservation of Ontario provincial highways in the areas of Corridor Investment Plans (CIP). The paper starts with an introduction to the Asset Management Business Framework (AMBF) that highlights some of the key system components and investment analysis tools applied in asset management. The paper then presents how MTO’s Second Generation Pavement Management System (PMS2) fits into the ongoing Asset Management (AM) activities in terms of corridor investment plans (CIP) and multi-year rehabilitation and maintenance program, with emphases on pavement performance measures, commonly used pavement rehabilitation treatments in Ontario, observed pavement performance and prediction models, integration of preventative maintenance strategies with rehabilitation treatments. An example application of a corridor investment plan is used to illustrate the key characteristics and functional capacities of the newly enhanced PMS2, including road network database management, performance evaluation and prediction, and customized decision trees based upon forecasted road conditions and targeted service levels. The

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.540

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 head0.007
GPT teacher head0.172
Teacher spread0.164 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

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