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Record W2087499916 · doi:10.1177/1045389x06074572

Civionics — A New Paradigm in Design, Evaluation, and Risk Analysis of Civil Structures

2007· article· en· W2087499916 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2007
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of SaskatchewanGovernment of ManitobaResearch Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural health monitoringEngineeringConstruction engineeringRisk analysis (engineering)Civil engineeringTerm (time)Civil infrastructureStructural systemSystems engineeringStructural engineering

Abstract

fetched live from OpenAlex

This article discusses the reasons why civil engineers are very conservative in the design of new structures and the evaluation of existing structures. It is argued that structural health monitoring (SHM) will assist in providing data that could be used to fine-tune the calibration of load and strength factors leading to more efficient and economical designs and better utilization of the strengths of existing structures. For major changes in design, construction, and evaluation to be accepted, it is necessary that innovative structures be monitored for their health so that the required data bank can be developed. To assist in achieving this goal, civil engineers in Canada are developing a new discipline, which integrates civil engineering and electrophotonics under the combined term `civionics'.

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.

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)0.001
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 score0.719
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.030
GPT teacher head0.312
Teacher spread0.281 · 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