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Record W2134181680 · doi:10.1193/070113eqs184m

NGA‐West2 Equations for Predicting PGA, PGV, and 5% Damped PSA for Shallow Crustal Earthquakes

2013· article· en· W2134181680 on OpenAlexafffund
David M. Boore, Jonathan P. Stewart, Emel Seyhan, Gail M. Atkinson

Bibliographic record

VenueEarthquake Spectra · 2013
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCalifornia Earthquake AuthorityU.S. Geological SurveyCalifornia Department of Transportation
KeywordsSeismologyGeologyGround motionTectonicsStructural basinRange (aeronautics)Event (particle physics)GeodesyTerm (time)Nonlinear systemPhysicsGeomorphology

Abstract

fetched live from OpenAlex

We provide ground motion prediction equations for computing medians and standard deviations of average horizontal component intensity measures (IMs) for shallow crustal earthquakes in active tectonic regions. The equations were derived from a global database with M 3.0–7.9 events. We derived equations for the primary M ‐ and distance‐dependence of the IMs after fixing the V S 30 ‐based nonlinear site term from a parallel NGA‐West2 study. We then evaluated additional effects using mixed effects residuals analysis, which revealed no trends with source depth over the M range of interest, indistinct Class 1 and 2 event IMs, and basin depth effects that increase and decrease long‐period IMs for depths larger and smaller, respectively, than means from regional V S 30 ‐depth relations. Our aleatory variability model captures decreasing between‐event variability with M , as well as within‐event variability that increases or decreases with M depending on period, increases with distance, and decreases for soft sites.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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
GenreMethods

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

Citations1,717
Published2013
Admission routes2
Has abstractyes

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