Evaluation of reduced computational approaches to assessment of tsunami hazard and loss using stochastic source models: Case study for Tofino, British Columbia, Canada, subjected to Cascadia megathrust earthquakes
Bibliographic record
Abstract
Probabilistic tsunami hazard and risk analyses are important decision support tools in developing tsunami risk reduction strategies and actions for coastal communities. A stochastic source modeling method facilitates the consideration of uncertainties associated with earthquake rupture processes. However, the computational costs are high when inland tsunami inundation and building damage need to be evaluated accurately. To develop a practical solution by keeping the computational requirements at a manageable level, probabilistic tsunami hazard analysis based on low‐resolution tsunami simulations but considering a wide range of possible earthquake ruptures can be used to identify smaller sets of stochastic rupture models for target probability levels. These identified stochastic rupture models can be used to obtain the estimates of tsunami building loss by running high‐resolution tsunami inundation simulations. A case study is set up for Tofino, British Columbia, Canada, under the potential tsunami threat from the Cascadia megathrust earthquakes to investigate the correlation between the maximum modeled tsunami wave amplitudes at offshore locations and the tsunami building loss. A practical solution is proposed to obtain the tsunami risk estimates based on a limited number of high‐resolution tsunami inundation simulations, thus reducing the computational costs for the probabilistic tsunami risk analysis. The effectiveness of the approach is demonstrated by comparing the median value of the tsunami risk estimates from 20 stochastic rupture model simulations that are selected based on probabilistic tsunami hazard analysis for a representative offshore location using the low‐resolution tsunami simulations (i.e. 270 m grids) with the full probabilistic tsunami risk analysis of the target building portfolio based on the 1200 high‐resolution tsunami simulations (i.e. 5 m grids).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".