Stochastic Source Modelling and Tsunami Analysis of the 2012 Mw 7.8 Haida Gwaii Earthquake
Bibliographic record
Abstract
The Mw 7.8 2012 Haida Gwaii Earthquake triggered a tsunami that highlighted the importance of tsunami hazard assessment on Canada’s Pacific coast. Stochastic source modelling serves as a valuable method to assess future tsunami hazard and has not been performed for this region. The source models characterize the uncertainty of earthquake ruptures by considering variability in fault geometry and slip heterogeneity, which, in turn, allows the consideration of a wide range of tsunami scenarios in the Haida Gwaii region. The model predictions are constrained by observational data and past source inversion studies. One hundred twenty-eight stochastic tsunami scenarios are generated using the stochastic source modelling method to assess tsunami hazard via tsunami inundation simulations of the target region and conduct sensitivity analyses of tsunami height variability. The resulting models can promote better-informed risk management decisions and future probabilistic tsunami hazard analysis in this region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".