An empirical perspective on uncertainty in earthquake ground motion prediction1This paper is one of a selection of papers in this Special Issue in honour of Professor Davenport.
Bibliographic record
Abstract
Uncertainty in the ground-motion amplitudes that will be realized from earthquakes at a given magnitude and distance plays a critical role in seismic hazard analysis. Examination of ground-motion variability from an empirical perspective can be used to characterize its epistemic and aleatory components, which are implicitly coupled, in a self-consistent manner that minimizes “double-counting” of uncertainty. This study empirically evaluates both components of uncertainty for shallow crustal earthquakes in active tectonic regions as deduced from the NGA (Next Generation Attenuation) database. The epistemic component of uncertainty, which I argue should include all inter-event variability, is estimated to be about 0.2 log(10) units near the source, and grows with increasing distance; the growth with distance may be due to regional attenuation variability, but may also be partly due to the relative paucity of near-source observations. The aleatory component (commonly referred to as ‘sigma’), which I argue sh...
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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 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".