Effect of calibration data on performance of tsunami early warning model
Bibliographic record
Abstract
Data-driven tsunami early warning systems can be calibrated using possible wave profiles that are simulated from numerous hypothetical rupture scenarios. However, tsunami wave profiles that are simulated based on a certain synthesis method may not capture future situations comprehensively. To quantify the effects of calibration datasets on tsunami early warning models, a case study focusing on Vancouver Island that faces major tsunami threats from the Cascadia subduction earthquakes is explored. Two tsunami wave databases are generated by considering a logic tree model of potential tsunami sources for probabilistic tsunami hazard analysis and stochastic rupture sources with variable geometry and heterogeneous slip distribution. Tsunami early warning models are developed based on three fitting methods, namely, multiple linear regression, random forest, and neural network. Using consistent and inconsistent training-testing (calibration-evaluation) datasets, performances of the tsunami early warning models are compared. The results of the comparative analyses indicate that the use of random forest and neural network outperform conventional multiple linear regression methods. The effects of calibration data on the model performance are significant and may not be captured well by a conventional cross-validation scheme. This study highlights the importance of epistemic uncertainty of the tsunami early warning model performance.
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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 | 0.010 | 0.051 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".