METIS PROJECT: GEM'S CONTRIBUTIONS TO THE HAZARD WORK PACKAGE
Bibliographic record
Abstract
In this contribution, we illustrate improvements made to the OpenQuake Engine - the open-source hazard and risk calculation engine developed by GEM - by the GEM hazard team in the context of the METIS project. These include new approaches for the propagation of epistemic uncertainties, a new approach for the calculation of Vector-valued PSHA, the implementation of the Method 4 proposed by Lin et al. [2013] and the ability to compute seismic hazard by taking into account the contribution of aftershocks. Regarding the propagation of epistemic uncertainties, we improved the capabilities of the OQ Engine to process logic trees by adding the option of using a Latin Hypercube sampling approach in lieu of the more traditional Monte Carlo one and we included the possibility of specifying where to apply the weights assigned to the various realizations admitted. We also proposed a new way to propagate epistemic uncertainties that calculates seismic hazard for each individual source and combines the results in a post-processing phase by using discrete distributions and a convolution approach. This new method is computationally efficient and it provides results consistent with the ones provided by more traditional approaches. With respect to the calculation of the Conditional Spectrum, we implemented in the OQ Engine the most complete, rigorous and complex approach available for the calculation of the spectrum, the so-called method 4 from Lin et al. [2013]. We also developed a new approach for the calculation of VPSHA that combines the logic used by the so-called ‘indirect’ approach with a higher precision of the results computed. The last topic considered is modelling seismic hazard by considering aftershocks’ contributions. In this case, we implemented tools for adjusting the rates of existing main shocks based on input models for the OQ Engine to account for the contribution of aftershocks. We added to the OQ Engine functions allowing the computation of hazard using these models.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.025 |
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".