A Test of the Applicability of NGA Models to the Strong Ground-Motion Data in the Iranian Plateau
Bibliographic record
Abstract
The Next Generation Attenuation (NGA) project has now published several new sets of empirical ground-motion prediction equations (GMPEs) for PGA, PGV, and response spectral ordinates. These models significantly advance the state-of-the-art empirical ground-motion modeling and account for many effects that have not been directly accounted for in the existing Iranian GMPEs. Assuming that the present strong-motion database in Iran is unlikely to drastically change in the near future, the question we ask in this study is: Can the NGA models be applied in Iran? In order to answer this question, the NGA models of CitationBoore and Atkinson [2008], CitationCampbell and Bozorgnia, [2008], and CitationChiou and Youngs [2008], which are shown to be representative of all NGA models, are compared with the Iranian strong-motion database. The database used in this study comprises 863 two-component horizontal acceleration time series recorded within 100 km of epicentral distances for 166 earthquakes in Iran with magnitudes ranging from 4.0–7.4. The comparisons are made using analyses of residuals. The analysis indicates that the NGA models may confidently be applied within the Iranian plateau. To provide more reliable constraint on finite-fault effects and nonlinear site response in the Iranian equations, it would be useful to drive new GMPEs based on a merger of the NGA and Iranian databases.
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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 | 0.016 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".