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Prioritization Framework for Asset Management of New Brunswick’s Covered Bridges

2020· article· en· W3017857131 on OpenAlexaffabout
Simon Bush, Angela Dean, C Michael White, J. Lamrock

Bibliographic record

VenueJournal of Performance of Constructed Facilities · 2020
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsGovernment of New BrunswickUniversity of New BrunswickWSP (Canada)Bishop's UniversityEXP (Canada)
Fundersnot available
KeywordsPrioritizationAsset managementAsset (computer security)EngineeringRisk analysis (engineering)Forensic engineeringComputer scienceConstruction engineeringBusinessManagement scienceComputer securityFinance

Abstract

fetched live from OpenAlex

Heritage bridges, such as covered bridges, provide a connection to a past time and engineering methods, and form part of the architectural landscape. Accordingly, quality examples of heritage bridges should be maintained for future generations. To support the preservation of New Brunswick’s covered bridges, a novel prioritization framework was developed. The framework assesses both the network link importance and the social importance of each bridge. Four overarching strategies were then developed based on the social-link ratings. Based on the ranking of each bridge, one of the four strategies is used to inform the future management of each of the 54 covered bridges located on New Brunswick’s road network. The strategies include preservation of the existing bridge and movement of the traffic to a new bridge, preservation of the bridge and closure to traffic, decommissioning the existing bridge and replacement with a new bridge, and decommissioning of the existing bridge and removal of the highway link. How the strategy was developed and the components that form part of the assessment framework are described herein. The framework is currently being used to facilitate the transition of the exemplar covered bridges from active highway assets to lasting heritage sites.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.493

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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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 designBench or experimental
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

Citations4
Published2020
Admission routes2
Has abstractyes

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