Potential Fault Displacement Hazard Assessment Using Stochastic Source Models: A Retrospective Evaluation for the 1999 Hector Mine Earthquake
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Surface fault displacement due to an earthquake affects buildings and infrastructure in the near-fault area significantly. Although approaches for probabilistic fault displacement hazard analysis have been developed and applied in practice, there are several limitations that prevent fault displacement hazard assessments for multiple locations simultaneously in a physically consistent manner. This study proposes an alternative approach that is based on stochastic source modelling and fault displacement analysis using Okada equations. The proposed method evaluates the fault displacement hazard potential due to a fault rupture. The developed method is applied to the 1999 Hector Mine earthquake from a retrospective perspective. The stochastic-source-based fault displacement hazard analysis method successfully identifies multiple source models that predict fault displacements in close agreement with observed GPS displacement vectors and displacement offsets along the fault trace. The case study for the 1999 Hector Mine earthquake demonstrates that the proposed stochastic-source-based method is a viable option in conducting probabilistic fault displacement hazard analysis.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it