Uncertainty of probabilistic tsunami hazard assessment of Zihuatanejo (Mexico) due to the representation of tsunami variability
Bibliographic record
Abstract
This study conducts a probabilistic tsunami hazard assessment (PTHA) and compares two approaches to representing earthquake source variability in the PTHA. The target region is the coast of Zihuatanejo in the State of Guerrero, Mexico. First, numerous synthetic fault slip distributions are generated using a stochastic random-phase process. The moment magnitude ranges from 7.8 to 8.6. A numerical tsunami simulation is implemented for each earthquake fault slip. The result of the Monte Carlo simulation indicates the tsunami heights at the nearshore of city areas tend to be higher. Then, the exceedance probabilities of tsunami height are estimated and compared using two different PTHA approaches: the random phase approach and the logic tree approach. The logic tree can generally incorporate many types of uncertainty, but this study focuses on the earthquake source uncertainty for comparison. The comparison result indicates significant differences between the two tsunami hazard models. Additionally, the logic tree approach is used to investigate the possible ranges in tsunami heights for extreme events by assuming that a sizable epistemic uncertainty exists in a given region. The tsunami heights for a 1,000-year event vary significantly when the weighting values for the paths in the logic tree are changed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".