CHARACTERIZING EARTHQUAKE RISK IN CANADA: A MODEL 18 YEARS IN THE MAKING
Bibliographic record
Abstract
The Geological Survey of Canada recently published a national seismic risk assessment, quantifying the risk from earthquakes at the neighborhood level across Canada. This includes both probabilistic results and a scenario (deterministic) catalogue. Achieving this involved creation of national exposure and fragility models, and slight modifications to the sixth-generation national seismic hazard model for Canada. This article will highlight the years-long process that made this possible, outline the methodology at a high level, and portray selected results. Probabilistic results include average annual losses and national loss exceedance curves, considering economic and life-loss risks. We find that national 500-year economic losses may be greater than the capacity of the insurance sector to absorb financial consequences, based on recent reporting from within that industry. We also present results of a Seismic Risk Index, a measure which factors in both physical and social dimensions of seismic risk and helps identify communities most at risk from earthquakes in Canada, both in absolute and normalized terms. We find that major urban centers in southwestern British Columbia, and along the St. Lawrence Lowlands are at the highest risk from earthquakes when considering absolute losses, while small and often remote communities in western Canada are at the highest risk when considering normalized losses. In addition to probabilistic results, we present results from our deterministic scenario catalogue, including additional metrics such as the number of casualties at different levels of severity and disruption to housing. The catalogue of earthquake scenarios is intended to grow, per a new systemic approach to scenario generation. Finally, we outline efforts to communicate this risk to decision makers. This includes a custom website, RiskProfiler.ca, which was designed to allow stakeholders to visualize and explore the results of the national seismic risk assessment in a user-friendly way. It includes maps and charts, allowing users to consider the impact of retrofit measures on some or all building types. We anticipate that this site will be of use to those in the planning, insurance, and emergency management sectors, and of interest to those engaged in seismic retrofit and earthquake engineering in Canada. It may also interest those from other countries who are developing similar platforms for risk reduction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".