EARTHQUAKE-TSUNAMI INSURANCE RISK EXPOSURE ESTIMATION FOR THE DISTRICT OF TOFINO, BRITISH COLUMBIA
Bibliographic record
Abstract
Southwestern British Columbia in Canada has a high likelihood of facing significant earthquake threats in the future. It is exposed to significant seismic and tsunami hazards, originating from the Cascadia subduction zone. A scenario of particular concern to residents and emergency managers is the future occurrence of a moment magnitude (M) 9.0-class megathrust earthquake in Cascadia. The scientific challenges in assessing the potential hazards and risks for residents and assets in British Columbia include the characterization of the earthquake rupture of future major Cascadia events and the modeling of their multi-hazard cascades which affect the population and built environment simultaneously. In addition, there are other significant seismic risk contributions from shallow crustal and deep inslab earthquakes. To protect households from potential financial risks due to the Cascadia and other seismic events via insurance, it is necessary to quantify the monetary losses from ground shaking and tsunamis. However, such multi-hazard risk assessments have not been carried out to determine insurance premiums (or annual expected loss) and to quantify tail risks. This study presents a probabilistic earthquake-tsunami loss model for the Cascadia subduction zone by focusing on the District of Tofino, British Columbia. The earthquake occurrence and rupture models for the Cascadia subduction zone are developed by incorporating the time-dependency of earthquake occurrence and by adopting a stochastic source modeling approach, which allows considering heterogeneous earthquake slip distributions. The results produce single-hazard and multi-hazard exceedance probability loss curves for earthquakes and tsunamis from the Cascadia subduction zone. In addition, earthquake risks due to other seismic sources are integrated with the above-mentioned multi-hazard loss curves due to the Cascadia events. In this way, a complete financial exposure from all major seismic and tsunami sources is evaluated. These loss curves can be used for determining the insurance rates for multi-hazard and multi-source risk coverage. The results are beneficial for providing more risk financing options via insurance against the future Cascadia and other seismic events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".