Significance of site natural period effects for linear site amplification in central and eastern North America: Empirical and simulation‐based models
Bibliographic record
Abstract
This article evaluates linear simulation‐based and empirical site amplification models including site natural period dependency parameters to account for the distinctive amplification behavior near site fundamental frequencies resulting from the sharp impedance contrast between soil and underlying hard bedrock in central and eastern North America (CENA). The simulation‐based amplification models are developed using 581,685 frequency‐domain linear analyses generated from a parametric study and include V S 30 ‐scaling and site natural period ( T nat ) parameters. The empirical models are derived from residuals analyses of ground‐motion models for two reference conditions: B/C boundary ( V S 30 = 760 m/s) and CENA hard‐rock condition ( V S = 3000 m/s). The simulation‐based and empirical models are compared for 8 site profiles in CENA to measured horizontal‐to‐vertical (H/V) component response spectral (RS) ratios, the mean of linear simulations for similar sites, and one‐dimensional (1D) linear site response analysis for four of these sites. Comparisons between observed and estimated site amplification behaviors highlight model dependency on T nat in CENA. Model consistencies and differences related to the distinct linear amplification features near site fundamental frequency are discussed.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".