Calculating modal target epsilons from multiple GMPE models: A case study for Montréal
Bibliographic record
Abstract
In Probabilistic Seismic Hazard Analysis (PSHA), the target epsilon "εo" represents the departure of the target ground motion, at a specific hazard level, from that predicted by the ground-motion prediction equation (GMPE). Target epsilon values are commonly used in seismic performance evaluations for identifying design events and selecting ground motion records for vulnerability and liquefaction studies. There are few methods for calculating the modal (most likely) target epsilons and values are usually reported at different locations by considering a single GMPE in the seismological model. A single GMPE model provides an independent set of epsilons. For cases where multiple GMPEs are being considered, a method is required to calculate robust weighted modal epsilon values that account for epistemic uncertainty. A method is proposed for calculating target epsilons when multiple GMPE models are being considered. The proposed method, along with other procedures, is demonstrated for the case of Montreal Island.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".