Ground-Motion Evaluation of Moderate and Large Interface Earthquakes along the Chilean Subduction Zone
Bibliographic record
Abstract
ABSTRACT Strong-motion observations of recent interface earthquakes along the Chilean subduction zone are evaluated with two ground-motion models (GMM). One GMM was developed with Chilean data and the other with global data. The GMM developed with local Chilean data is found to have an overall better prediction performance than the GMM developed using a global data set. Using residual analysis with the Chilean GMM as reference model due to its better performance, clear indications of an increase of short-period radiation for deeper earthquakes in north and central Chile were found, which may be related to frictional features on the interface such as interseismic coupling, as found previously for other regions, such as Japan. Also, the Iquique earthquake, which featured a clear precursory slow-slip event, exhibits mostly negative between-event residuals at short periods for earthquakes before and after the mainshock, indicating predominantly weaker short-period radiation. However, this trend is not observed in the aftershock sequence of the Illapel earthquake, which did not feature a significant slow-slip event nor precursory seismicity in its rupture area. Finally, a poor predictive performance was found for the Chilean GMM in southern Chile, overpredicting most of the observations. Based on these results, it is proposed that future local GMMs should include corrections for depth, regional effects and include earthquakes from southern Chile, as new data are becoming available 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".