Multi‐hazard parametric catastrophe bond trigger design for subduction earthquakes and tsunamis
Why this work is in the frame
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Bibliographic record
Abstract
This study presents trigger design methods and performance evaluations of multi‐hazard parametric catastrophe bonds for mega‐thrust subduction earthquakes and tsunamis. The catastrophe bonds serve as alternative disaster risk financing tools for insurers and reinsurers as well as municipalities and governments. Two types of parametric catastrophe bond trigger are investigated. A scenario‐based method utilizes available earthquake source‐based information, such as magnitude and location, whereas a station‐intensity‐based method can be implemented when seismic and tsunami hazard monitoring systems are in place in a region. The case study results, focusing on wooden buildings in Miyagi Prefecture, indicate that the station‐intensity‐based trigger methods outperform the scenario‐based trigger methods significantly. Incorporating seismic and tsunami hazard information from multiple recording stations results in smaller trigger errors. The station‐intensity‐based methods are applicable to building portfolios at both municipality levels and regional levels.
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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 | 0.000 | 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 it