A national seismic risk model for Canada: Methodology and scientific basis
Bibliographic record
Abstract
Canada is exposed to rare but potentially destructive earthquakes that threaten densely settled metropolitan centers in many parts of the country. To assess the impacts and consequences of future natural‐hazard events and help advance policy goals and objectives of the Sendai Framework for Disaster Risk Reduction, Natural Resources Canada, through a collaborative partnership with the Global Earthquake Model Foundation, produced a national seismic risk model. Developing this model has required the creation of a national exposure inventory, Canadian‐specific fragility and vulnerability curves, and significant simplification of the Canadian Seismic Hazard Model which forms the basis for the design seismic hazard values of the National Building Code of Canada. Using the Global Earthquake Model Foundation’s OpenQuake Engine, probabilistic stochastic risk modeling is completed under baseline and simulated retrofit conditions to assess seismic risk at the neighborhood level for all settled areas in Canada. Output risk metrics include the expected immediate physical impacts of earthquake events such as building damage, casualties, and direct economic losses. This article documents the technical details of the modeling approach including a description of novel data sets in use, a summary of the extensive sensitivity testing undertaken, and characterization of quality control implemented in the absence of usable validating earthquake loss data. The results from this model, such as loss exceedance curves and annual average losses, provide an open, accessible and quantitative base of evidence for decision‐making at local, regional, and national levels. As a large country with a complex seismic hazard model and dispersed populations, this Canadian study is unique. However, the challenges faced and solutions offered are likely to be of interest to other nations pursuing similar programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".