Multi-hazard insurance premium rate-making using earthquake-tsunami risk model for the district of Tofino, British Columbia, Canada
Bibliographic record
Abstract
<!--!introduction!--> Canada has a high likelihood of facing significant earthquake threats in the future. On the Pacific coast, southwestern British Columbia 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. To protect households from potential financial risks due to the Cascadia events via insurance, it is necessary to quantify the monetary losses from ground shaking and tsunami. However, such multi-hazard risk assessments have not be carried out to determine insurance premiums. 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 strong motions and tsunamis. These loss curves can be used for determining the insurance rates for the multi-hazard risk coverage for Tofino. The results are beneficial for providing more risk financing options via insurance against the future Cascadia events.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".