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Record W3170721888 · doi:10.1177/87552930211007503

NGA‐East ground‐motion characterization model Part II: Implementation and hazard implications

2021· article· en· W3170721888 on OpenAlexaff
Robert Youngs, Christine Goulet, Yousef Bozorgnia, Nicolas Kuehn, Linda Al Atik, Robert Graves, Gail M. Atkinson

Bibliographic record

VenueEarthquake Spectra · 2021
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern University
FundersElectric Power Research InstituteU.S. Nuclear Regulatory CommissionU.S. Geological SurveyU.S. Department of Energy
KeywordsSeismic hazardGround motionHazardSeismologyHazard analysisCommissionSeismic riskGeologyCivil engineeringForensic engineeringEngineeringLawReliability engineeringPolitical science

Abstract

fetched live from OpenAlex

As a companion article to Goulet et al., we describe implementation of the NGA‐East ground motion characterization (GMC) model in probabilistic seismic hazard analysis (PSHA) for sites in the Central and Eastern United States (CEUS). We present extensions to the EPRI/DOE/NRC seismic source characterization (SSC) model for the CEUS needed for full implementation of NGA‐East. Comparisons are presented to the EPRI GMC, the currently accepted model by the U.S. Nuclear Regulatory Commission for hazard assessment at nuclear facilities. Comparisons are presented both in terms of GMC model components and in the resulting seismic hazard assessments for a range of site locations in the CEUS. Illustrations of the effect of various components of the NGA‐East GMC on seismic hazard results are also presented. Finally, we present recommendations for application of the NGA‐East GMC in PSHA.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.068
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.232
Teacher spread0.217 · 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 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

Citations14
Published2021
Admission routes1
Has abstractyes

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