NGA‐subduction global ground motion models with regional adjustment factors
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Bibliographic record
Abstract
We develop semi‐empirical ground motion models (GMMs) for peak ground acceleration, peak ground velocity, and 5%‐damped pseudo‐spectral accelerations for periods from 0.01 to 10 s, for the median orientation‐independent horizontal component of subduction earthquake ground motion. The GMMs are applicable to interface and intraslab subduction earthquakes in Japan, Taiwan, Mexico, Central America, South America, Alaska, the Aleutian Islands, and Cascadia. The GMMs are developed using a combination of data inspection, data regression with respect to physics‐informed functions, ground‐motion simulations, and geometrical constraints for certain model components. The GMMs capture observed differences in source and path effects for interface and intraslab events, conditioned on moment magnitude, rupture distance, and hypocentral depth. Site effect and aleatory variability models are shared between event types. Regionalized GMM components include the model constant (that controls ground motion amplitude), anelastic attenuation, magnitude‐scaling break point, linear site response, and sediment depth terms. We develop models for the aleatory between‐event variability , within‐event variability , single‐station within‐event variability , and site‐to‐site variability . Ergodic analyses should use the median GMM and aleatory variability computed using the between‐event and within‐event variability models. An analysis incorporating non‐ergodic site response should use the median GMM at the reference shear‐wave velocity condition, a site‐specific site response model, and aleatory variability computed using the between‐event and single‐station within‐event variability models. Epistemic uncertainty in the median model is represented by standard deviations on the regional model constants, which facilitates scaled‐backbone representations of model uncertainty in hazard analyses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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