Malawi probabilistic seismic hazard analysis (PSHA) using the Malawi Seismogenic Source Model (MSSM). Supplementary Files v1.1
Bibliographic record
Abstract
Updated (October 2022) version of supplementary files for running probabilistic seismic hazard analysis (PSHA) MATLAB codes for Malawi. The PSHA codes themselves (v1.0) are available at: https://doi.org/10.5281/zenodo.7265781and the most recent version will be available on GitHub at: https://github.com/jack-williams1/Malawi_PSHA. Note the variables stored here are not stored on GitHub due to the file size. Includes both input files for performing PSHA and output ground motions for plotting PSHA results. Files are: malawi_Vs30_active.txt: Input USGS slope-based Vs30 values for Malawi (Wald and Allen 2007) EQCAT_comb.mat: MSSM Direct catalog for all possible rupture weightings (stored as MATLAB variable) GM_MSSM_em_20221027: Ground motions for plotting PSHA maps (stored as MATLAB variable) GM_MSSM_20221021.mat: Ground motions needed for plotting PSHA-site analysis figures (stored as MATLAB variable) mssm_comb.mat: Matlab file for combined MSSM Direct and Adapted MSSM catalogs (stored as MATLAB variable) MSSM_Catalog_Adapted_em.mat: Adapated MSSM event catalog (stored as MATLAB variable) syncat_bg.mat: Areal source stochastic event catalog (stored as MATLAB variable) Further descriptions of these files and how to use them are provided on Github. An open-access manuscript describing the PSHA is available at: Williams J. N., Werner M. J., Goda K., Wedmore L. N. J., De Risi R., Biggs J., Mdala H., Dulanya Z., Fagereng Å, Mphepo F., Chindandali P. (2023). Fault-based probabilistic seismic hazard analysis in regions with low strain rates and a thick seismogenic layer: a case study from Malawi, Geophysical Journal International, Volume 233, Issue 3, June 2023, Pages 2172–2206, https://doi.org/10.1093/gji/ggad060 Please reference this publication along with this repository when using these data. USGS vs30 value compilation described in: Allen, T. I., and Wald, D. J., 2009, On the use of high-resolution topographic data as a proxy for seismic site conditions (Vs30), Bulletin of the Seismological Society of America, 99, no. 2A, 935-943.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.279 | 0.060 |
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".