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Record W3198046973 · doi:10.4095/328860

Developing a retrofit scheme for Canada's Seismic Risk Model

2021· report· en· W3198046973 on OpenAlexaffabout
Tiegan Hobbs, J M Journeay, Philip LeSueur

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsRetrofittingSeismic riskUnreinforced masonry buildingMasonryInduced seismicityPlan (archaeology)Seismic retrofitRisk analysis (engineering)Function (biology)EngineeringProcess (computing)Civil engineeringComputer scienceConstruction engineeringBusinessReinforced concreteGeology

Abstract

fetched live from OpenAlex

The first published Canadian Seismic Risk Model, CanSRM1, is set for release this year. It considers the potential impact of seismicity on Canadian building stock and people, taking into account the current built environment. To understand the benefits of retrofit policies, policy makers need a base of evidence showing the difference in risk before and after retrofitting. Therefore, we plan to incorporate a simulated retrofit scenario into the national model, after consulting with practicing engineers and experts to better understand how such retrofits are likely to occur. This report presents the outcome of that consultation process, and recommendations for implementation into CanSRM1. It is apparent that only modest retrofits should be simulated for the majority of buildings, except those which are expected to serve a post-disaster function. Other building types are poorly suited for cost-effective retrofit, such as unreinforced masonry. These ideas are used to create a retrofit scenario across Canada, which can be used by policy makers to create targeted seismic risk reduction policies.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.138
Threshold uncertainty score0.952

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 head0.037
GPT teacher head0.263
Teacher spread0.226 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations7
Published2021
Admission routes2
Has abstractyes

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