Regionally Adjustable Generic Ground‐Motion Prediction Equation Based on Equivalent Point‐Source Simulations: Application to Central and Eastern North America
Bibliographic record
Abstract
Abstract We develop a generic ground-motion prediction equation (GMPE) that can be adjusted for use in any region by modifying a few key model parameters. The basis of the GMPE is an equivalent point-source simulation model whose parameters have been calibrated to empirical data in California, in such a way as to determine the decoupled effects of basic source and attenuation parameters on ground-motion am-plitudes. We formulate the generic GMPE as a function of magnitude, distance, stress parameter, geometrical spreading rate, and anelastic attenuation. This provides a fully adjustable predictive model, allowing users to calibrate its parameters using observed motions in the target region. We also include an empirical calibration factor to account for residual effects that are different from and/or missing in simulations compared to observed motions in the target region. As an example, we show how the generic GMPE can be adjusted for use in central and eastern North America (CENA), and calibrated with the Next Generation Attenuation-East database. We provide median predictions of ground motions in CENA for average horizontal-component peak ground motions and 5 % damped pseudospectral acceleration (periods up to T 10 s), for magnitudes M 3–8 and distances up to 600 km.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".