Benchmarking Scenario Performance in the First Generation Canadian Seismic Risk Assessment
Bibliographic record
Abstract
Disaster risk reduction relies on quantitative estimates of the future impacts and consequences of known hazard threats in order to evaluate proposed mitigation and adaptation measures. Natural Resources Canada is collaborating with the Global Earthquake Model Foundation on the first ever national seismic risk assessment in Canada to inform disaster risk reduction planning by individuals, businesses and organizations working across all jurisdictional levels. The 2020 National Seismic Risk Model incorporates the 6th Generation National Seismic Hazard Map, a novel physical exposure model for the entire country, localized exposure models based on a machine learning approach to building categorization, and HAZUS-based earthquake building performance functions. Before results can be transmitted to end users, the model must be validated in a Canadian context using observations from real world disaster events or pre-existing catastrophic risk models. This study focuses on benchmarking the 2020 Canadian National Seismic Risk Model using shaking intensities and physical impacts recorded from the 2001 Mw 6.8 Nisqually and 2012 Mw 7.8 Haida Gwaii events, and the results of a 2013 catastrophic risk assessment performed by AIR Worldwide to evaluate the potential impact of major earthquakes in eastern Quebec and Cascadia. We compute anticipated building damage, economic loss, and fatalities for these benchmark scenario earthquakes using the OpenQuake engine and the national exposure dataset. Preliminary results indicate that the model results are largely consistent with observed or predicted impacts of these earthquakes in Canada, after adjusting for economic and population growth. Subsequently, we will evaluate the impact of running the Cascadia scenario using a regional building-level exposure database versus the national level inventory. Ultimately, this work will assess the ability of the National Seismic Risk Assessment to reproduce expected results, to ensure the applicability of this model in anticipating future outcomes at the national and local level.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".