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Record W4393902325 · doi:10.1177/87552930241236765

Ranking and developing ground‐motion models for Southeastern Africa

2024· article· en· W4393902325 on OpenAlexaff
Joanna M. Holmgren, Maximilian J. Werner, Katsuichiro Goda, Manuela Villani, Vítor Silva, P. R. N. Chindandali

Bibliographic record

VenueEarthquake Spectra · 2024
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsGround motionGeologyRiftSeismologyContinental marginResidualRanking (information retrieval)TectonicsArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

The southern East African Rift System (EARS) is an early‐stage continental rift with a deep seismogenic zone. It is associated with a low‐to‐moderate seismic hazard, but due to its short and sparse instrumental record, there is a lack of ground‐motion studies in the region. Instead, seismic hazard assessments have commonly relied on a combination of active crustal and stable continental ground‐motion models (GMMs) from other regions without accounting for the unusual geological setting of this region and evaluating their suitability. Here, we use a newly compiled southern EARS ground‐motion database to compare six active crustal GMMs and four stable continental GMMs. We find that the active crustal GMMs tend to underestimate the ground‐motion intensities observed, while the stable continental GMMs overestimate them. This is particularly pronounced in the high‐frequency intensity measures (>5 Hz). We also use the referenced empirical approach and develop a new region‐specific GMM for southern EARS. Both the ranked GMMs and our new GMM result in large residual variabilities, highlighting the need for local geotechnical information to better constrain site conditions.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.216
Teacher spread0.196 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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