The Interface Between Empirical and Simulation-Based Ground-Motion Models
Bibliographic record
Abstract
Ground-motion models (GMMs) are a key driver for the results of probabilistic seismic hazard analyses and their uncertainty. GMMs that bridge seismological and empirical approaches are an effective tool to represent the distribution of ground motion and its uncertainty in seismic hazard assessment. A methodology is presented that uses ground-motion data recorded at seismograph sites in eastern North America and shows how they can be used to calibrate simple scalable seismological models of ground-motion generation and propagation. Such GMMs can directly account for the gross features of source scaling (magnitude and stress parameter), attenuation, site response, and kappa effects. It is shown that, by application of appropriate GMM strategies, sigma (aleatory uncertainty) could be greatly reduced, resulting in lower calculated hazard for nuclear plants founded on rock. This reduction in sigma requires that high-quality seismic monitoring (e.g., broadband seismograph stations) be installed and operated over a period of years (in addition to strong-motion stations), and that an ongoing investment be made in data analysis and targeted GMM development using the data.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".