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Record W7115747978 · doi:10.71846/18-wcee-1753

NATIONAL SEISMIC HAZARD ASSESSMENTS IN CANADA: APPLICATIONS, PROGRESS AND FUTURE CHALLENGES

2025· article· en· W7115747978 on OpenAlexaboutno aff

Bibliographic record

VenueWorld Conference of Earthquake Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSeismic riskEarthquake scenarioSeismic hazardHazardUrban seismic riskProbabilistic logicInduced seismicity

Abstract

fetched live from OpenAlex

National seismic hazard models form the foundation of earthquake risk reduction strategies to minimize human casualties and economic losses from future earthquakes. Natural Resources Canada and its predecessors have been generating national estimates of seismic hazard in Canada for over 70 years. These national models have formed the basis of seismic design values for the National Building Code of Canada since 1953. As knowledge of seismicity in Canada has grown, and as probabilistic seismic hazard analyses have become more sophisticated, Canada's national mapping efforts have become increasingly more complex. Currently, the 6th Generation Seismic Hazard Model of Canada is the most advanced national assessment to date. The increased need to transition from quantitative hazard-based decisions to risk-based decision making has seen Canada's national seismic model incorporated into probabilistic national earthquake risk models. This paper will describe current challenges in estimating and modelling seismic hazard in Canada with a particular focus on 1) how models need to be adapted for suitability into either building code or national risk models and 2) updates on model improvements expected for future generations of models.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.903

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 head0.015
GPT teacher head0.227
Teacher spread0.212 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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