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Data acquisition, processing and management systems for a Canadian bridge monitoring project

2004· article· en· W2522926487 on OpenAlexaboutno aff
Mo Shing Cheung, Bahman Noruziaan, Wenchang Li

Bibliographic record

VenuePublication Server of Weimar Bauhaus-University (Weimar Bauhaus-University) · 2004
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsBridge (graph theory)Computer scienceMedicine

Abstract

fetched live from OpenAlex

This paper will present a number of technical aspects for one of the most elaborate instrumentation and data acquisition projects ever undertaken in Canada. Confederation Bridge, the longest bridge built over ice covered seawater has been equipped with the state of the art data acquistition devices and systems as well as data transfer networks. The Bridge has been providing a fixed surface connection between Prince Edward Island and Province of New Brunswick in Canada since its opening in 1997. The Bridge has a rather long design service life of 100 years. Because of its large size and long span length, its design is not covered by any existing codes or standards worldwide. The focus of the paper is to introduce the data acquisition, transfer, processing and management systems. The instrumentation and communications infrastructure and devices will be presented in some details along with the data processing and management systems and techniques. Teams of engineers and researchers use the collected data to verify the analysis and design assumptions and parameters as well as investigate the short-term and long-term behaviour and health of the Bridge. The collected data are also used in furthering research activities in the field of bridge engineering and in elevating our knowledge about behaviour, reliability and durability of such complex structures, their components and materials.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.025
GPT teacher head0.220
Teacher spread0.195 · 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.

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

Citations4
Published2004
Admission routes1
Has abstractyes

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