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Record W592331397

A Multi-Dimensional Sustainability Plan for Expressway Asset Preservation, Safety and Quality of Life

2008· article· en· W592331397 on OpenAlexaboutno aff
Andrew Dalziel, Chris Murray, Rick Andoga

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityAsset managementPlan (archaeology)BusinessAsset (computer security)Quality (philosophy)Environmental planningEnvironmental resource managementTransport engineeringRisk analysis (engineering)FinanceEngineeringComputer scienceEconomicsEnvironmental science
DOInot available

Abstract

fetched live from OpenAlex

The City of Hamilton, Ontario, is constructing an Expressway, which includes a number of subcomponents that must be considered if social, environmental, and financial benefits are to be maximized and costs minimized. The City evaluated the Expressway’s life-cycle requirements at the beginning of its life to ensure availability of sufficient long-term funding for this important infrastructure asset. A portion currently under construction is in an environmentally sensitive area. As such, the City has not only committed to long-term management of a significant transportation corridor, but to maintenance of this natural habitat. The City reviewed the sustainable funding requirements to efficiently manage these assets over a 100-year period. The resulting multi-dimensional sustainability plan offers a range of maintenance, rehabilitation, and reconstruction options, and preliminary budgetary envelopes. Environmental issues were also identified that must be considered as part of the ongoing management of these assets. This paper gives an overview of the unique nature of the Expressway and the developed

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.266
Threshold uncertainty score0.529

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.268
Teacher spread0.233 · 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 designNot applicable
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

Citations0
Published2008
Admission routes1
Has abstractyes

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