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Record W4415692686 · doi:10.1061/9780784486504.028

Regional Data Sets Included in the NGA-West3 Ground Motion Database

2025· article· W4415692686 on OpenAlexaff
Tristan E. Buckreis, Scott J. Brandenberg, Shako Mohammed, Chuckwuebuka C. Nweke, Rashid Shams, Mahdi Bahrampouri, Brendon Bradley, Jyun‐Yan Huang, Tadahiro Kishida, Giovanni Lanzano, Meibai Li, Lucia Luzi, Coro Miranda, Paolo Zimmaro, Yousef Bozorgnia, Jennifer L. Donahue, Jonathan P. Stewart

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWSP (Canada)
Fundersnot available
KeywordsGround motionMetadataData setSet (abstract data type)Motion (physics)Tectonics

Abstract

fetched live from OpenAlex

The Next Generation Attenuation (NGA)-West3 Program database builds upon that of NGA-West2 for shallow crustal earthquakes in active tectonic regimes to provide a robust data set to develop the next iteration of NGA ground motion models (GMMs). Researchers from Italy, Japan, New Zealand, Taiwan, United Arab Emirates, and the United States, amongst others, have collaborated to develop consistently processed data with uniform metadata from the respective regions, with data for other regions drawn from literature (e.g., Greece, Japan, and Türkiye). Over 80,000 three-component ground motions from 632 events with magnitudes generally greater than 4.0 across the Western United States (mostly in California) have been newly added, and the total database size is over 175,000 ground motions (generally three-component except for two-component records from KiK-Net stations in Japan). The database is being used by several teams of NGA GMM developers and will be publicly released sometime in 2025 when the NGA-West3 GMMs have been drafted.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.030
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.009
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.029

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 head0.053
GPT teacher head0.299
Teacher spread0.246 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2025
Admission routes1
Has abstractyes

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