Risk-based multi-hazard microzonation for earthquakes and tsunamis
Bibliographic record
Abstract
A multi-hazard spatial mapping for shaking and tsunami risks is necessary for coastal communities that face imminent dangers from megathrust subduction earthquakes. Conventional single-hazard (seismic or tsunami) mapping methods are not effective in addressing this research gap because the individual hazard effects cannot be combined directly. This study presents a new risk-based multi-hazard microzonation method, which displays relative risks of a representative building asset due to earthquakes and tsunamis within a local community and applies it to the District of Tofino in British Columbia, Canada, which is located in the Cascadia subduction zone. A recently developed catastrophe model is used to generate a comprehensive set of multi-hazard loss data for Tofino by reflecting two local factors, i.e., site amplification proxy for shaking and high-resolution topography for tsunami. The risk-based multi-hazard microzonation maps capture spatially varying loss contributions from shaking and tsunami hazards at different annual probability of exceedance levels. Different visualization approaches can be considered to facilitate the disaster risk management and risk communications with residents and stakeholders.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".