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Record W4393642529 · doi:10.5281/zenodo.6350793

Fault-based probabilistic seismic hazard analysis in regions with low strain rates and a thick seismogenic layer: a case study from Malawi. Supplementary Files

2022· dataset· en· W4393642529 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueExplore Bristol Research · 2022
Typedataset
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsWestern University
FundersResearch Councils UK
KeywordsSeismologyGeologyFault (geology)Probabilistic logicSeismic hazardLayer (electronics)HazardComputer scienceMaterials scienceArtificial intelligenceComposite material

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.234
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.050
GPT teacher head0.340
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it