Scientific basis of Canada's first public national seismic risk model
Bibliographic record
Abstract
Natural Resources Canada, in partnership with the Global Earthquake Model Foundation, has prepared a public Canadian Seismic Risk Model to support disaster risk reduction efforts across industry and all levels of government, and to aid in Canada's adoption of the Sendai Framework for Disaster Risk Reduction. Developing this model has involved the creation of a national exposure inventory, Canadian specific fragility and vulnerability curves, and adjustment of the Canadian Seismic Hazard Model which forms the basis for the seismic provisions of the National Building Code of Canada. Using the Global Earthquake Model Foundation's OpenQuake Engine (OQ), risk modelling is completed using both deterministic and probabilistic risk calculations, under baseline and simulated retrofit conditions. Output results are available in all settled regions of Canada, at the scale of a neighbourhood or smaller. We report on expected shaking damage to buildings, financial losses, fatalities, and other impacts such as housing disruption and the generation of debris. This paper documents the technical details of the modelling approach including a description of novel datasets in use, as well as preliminary results for a magnitude 9.0 earthquake on the Cascadia megathrust and nation-wide 500 year expected probabilistic losses. These kinds of results, such as earthquake scenario impacts, loss exceedance curves, and annual average losses, provide a quantitative base of evidence for decision making at local, regional, and national levels.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".