Probabilistic Characterization of Spatially Correlated Response Spectra for Earthquakes in Japan
Bibliographic record
Abstract
Seismic hazard and risk assessments of spatially distributed infrastructural systems require seismic demand models that capture random but correlated simultaneous seismic effects at multiple sites. This study characterizes spatially correlated ground-motion parameters probabilistically using comprehensive databases of the K-NET and KiK-net strong-motion networks in Japan by developing a ground-motion prediction equation and then investigating the correlation structure of regression residuals from the prediction equation. Analysis results indicate that (1) interevent residuals of ground-motion parameters at different vibration periods are more strongly correlated than intraevent and total residuals with zero separation distance; and (2) intraevent spatial correlation coefficients can be described as a simple exponential decay function that is independent of the way the event-based intraevent standard deviation is calculated, of the earthquake type, and of the vibration period. The developed overall correlation model of spatially correlated ground-motion parameters may be used for seismic hazard and risk assessments in a subduction environment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".