Fault-based probabilistic seismic hazard analysis in regions with low strain rates and a thick seismogenic layer: a case study from Malawi. Supplementary Files
Bibliographic record
Abstract
First release of supplementary files for running probabilistic seismic hazard analysis (PSHA) MATLAB codes for Malawi as uploaded to Github at: https://github.com/jack-williams1/Malawi_PSHA 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: MSSD Direct catalog for all possible rupture weightings (stored as MATLAB variable) GM_MSSD_em_20220302: Ground motions for plotting PSHA maps (stored as MATLAB variable) GM_MSSD_em_20220302.mat: Ground motions needed for plotting PSHA-site analysis figures (stored as MATLAB variable) mssd_comb.mat: Matlab file for combined MSSD Direct and Adapted MSSD catalogs (stored as MATLAB variable) MSSD_Catalog_Adapted_em.mat: Adapated MSSD 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. The PSHA is described in: Williams, J. N., Werner, M. J., Goda. K., Wedmore, L. N., De Risi R., Biggs, J., Mdala, H., Dulanya, Z., Fagereng, Å., Chindandali, P., Mphepo, F. (2022) Fault-based probabilistic seismic hazard analysis in regions with low strain rates and a thick seismogenic layer: a case study from Malawi. Submitted to Natural Hazards Please reference this publication along with this repository when using these data. When appropriate, we will update the citation to the manuscript. 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.044 | 0.003 |
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".