Fore-Arc and Back-Arc Ground Motion Prediction Model for Vrancea Intermediate Depth Seismic Source
Bibliographic record
Abstract
A next generation ground motion model for the prediction of spectral accelerations both in the fore-arc and back-arc regions of the Carpathians Mountains is developed in this research for the Vrancea intermediate depth seismic source in Romania. This ground motion prediction equation (GMPE) is an updated version of the model given in Vacareanu et al. [2014] and is applicable in both the fore-arc and the back-arc regions. The strong ground motion database from which the prediction model is derived consists of over 700 triaxial accelerograms from Vrancea subcrustal seismic events, as well as from other intermediate-depth earthquakes produced in other seismically active regions in the world. The applicability of this ground motion prediction model in both the fore-arc and the back-arc region is tested using the analysis of residuals. Moreover, the appropriateness of this GMPE for soil classes B and C defined in EN 1998-1, as well as for average soil conditions is investigated. All results suggest that this model is an improvement of the previous versions of ground motion prediction equations for Vrancea intermediate-depth seismic source and its use in both the fore-arc and the back-arc regions make it a reliable candidate for more accurate seismic hazard studies of Romania.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".