Empirical Evaluation of Aleatory and Epistemic Uncertainty in Eastern Ground Motions
Bibliographic record
Abstract
Horizontal-component response spectra data for ground mo-tions recorded on hard-rock sites in eastern North America (ENA) are used to explore the aleatory and epistemic uncer-tainty in ground-motion prediction equations (GMPEs). An all-station sigma, expressing the total calculated scatter of values about a GMPE, ranges from 0.25 to 0:29 log10 unit. Single-station sigmas, in which the scatter is evaluated station by station relative to a regional GMPE, average in the range of 0.23–0.28. The scatter of observations about site-specific GMPEs (GMPEs developed from multiple events recorded at a single station), which comes the closest to measuring the actual aleatory variability, has average values of 0.22–0.26. Overall, aleatory variability of ground motions in ENA is no larger than that for California, at least for moderate events re-corded on hard-rock sites. Epistemic uncertainty is considered by looking at the standard deviation of GMPEs developed sep-arately for each station (i.e., the scatter of predictions rather than the scatter of observations). This exercise suggests that the overall epistemic uncertainty in ENA GMPEs should be at least 0.15 log unit (as a standard deviation of the median GMPEs) in the magnitude–distance range in which the prediction equa-tions can be anchored by empirical data (magnitude <5:5, dis-tances>50 km). It should be larger than 0.15 unit at large magnitudes and close distances.
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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 | 0.021 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".