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Record W4416279819 · doi:10.26443/seismica.v4i2.1410

Vs30 of Coastal Cities in Eastern Indonesia in the Context of Earthquake Phenomenology

2025· article· en· W4416279819 on OpenAlexaff
Claire E. Ashcraft, Ron Harris, Julian Fretha, Abby Mangam, John H. McBride, Carolus Prasetyadi, Kevin A. Rey, Shayna Orme, Hanif Sulaeman, Diannitta Agustinawati, Bryce E. Berrett, Rachel Willmore, Ethan Westfall, Ian F. A. Bell, Giovani Cynthia Pradipta, Emma Baginski

Bibliographic record

VenueSeismica · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsFoothillsAlluviumVolcanoContext (archaeology)SubductionIndian oceanSeismic waveMercalli intensity scale

Abstract

fetched live from OpenAlex

Shear wave velocity measurements to 30 m depth (Vs30) using Multichannel Analysis of Surface Waves (MASW) indicate high seismic risk to the built environment in coastal cities adjacent to the Sunda and Banda subduction zones in Eastern Indonesia. These measurements were taken at 58 sites in the cities of Pacitan (southern Java), Lombok, Ambon, and the Banda Islands. Comparing Vs30 estimates with local geologic maps show low velocities associated mostly with unconsolidated alluvium on coastal plains where most of the built environment resides. Due to recent destructive earthquakes in two of the sites (2018 Lombok and 2019 Ambon) we were able to directly compare damage using the MMI scale to Vs30. In most cases the heavy damage was experienced in sites with low Vs30 values. The least damage was experienced near sites with high Vs30 values, which are in foothills underlain by shallow volcanic units. These data provide a way to quantify earthquake hazards in densely populated and rapidly developing regions of eastern Indonesia and provide foundational constraints for studies of resonance frequency.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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