Bibliographic record
Abstract
The deaggregation of seismic hazard is an effective way to identify scenario events that contribute to a selected seismic-hazard level. Depending on the use of the deaggregation results, the contribution may be defined as equal to or exceeding the selected hazard level. The deaggregation of seismic hazard should consider all uncertainty, aleatory uncertainty, and epistemic uncertainty. The identified scenario events can be used to check the responses of structures such as buildings. However, structures are constructed to provide service rather than just sustain environmental disturbances, and the seismic-risk assessment is at least as important and valuable as the seismic-hazard assessment for emergency preparedness planning and for the financial industry. Therefore, the notion of the deaggregation of seismic hazard is extended to that of seismic risk in the present study. Other issues investigated in this study are the impact of approximate treatment of epistemic uncertainty and the Cascadia subduction events on the deaggregation, and the differences in the identified scenario events from the deaggregation of seismic hazard and seismic risk. Numerical results suggest that the contribution of the uncertainty in attenuation relations to the deaggregation results can be very significant, and that the identified scenario events by deaggregating seismic hazard and seismic risk are somewhat different. Also, the elapsed time since the last major event for source zones whose earthquake occurrence is modeled as a renewal process can affect the deaggregation results significantly.
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 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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".