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Record W2113111458 · doi:10.1193/1.2924363

Comparisons of the NGA Ground‐Motion Relations

2008· article· en· W2113111458 on OpenAlexaff
Norman Abrahamson, Gail M. Atkinson, David M. Boore, Yousef Bozorgnia, Kenneth W. Campbell, Brian Chiou, I. M. Idriss, Walter Silva, Robert Youngs

Bibliographic record

VenueEarthquake Spectra · 2008
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsGeologySeismologyAftershockStandard deviationGround motionMagnitude (astronomy)Slip (aerodynamics)Strong ground motionTectonicsGeodesyStatisticsMathematics

Abstract

fetched live from OpenAlex

The data sets, model parameterizations, and results from the five NGA models for shallow crustal earthquakes in active tectonic regions are compared. A key difference in the data sets is the inclusion or exclusion of aftershocks. A comparison of the median spectral values for strike‐slip earthquakes shows that they are within a factor of 1.5 for magnitudes between 6.0 and 7.0 for distances less than 100 km . The differences increase to a factor of 2 for M5 and M8 earthquakes, for buried ruptures, and for distances greater than 100 km . For soil sites, the differences in the modeling of soil/sediment depth effects increase the range in the median long‐period spectral values for M7 strike‐slip earthquakes to a factor of 3. The five models have similar standard deviations for M6.5‐M7.5 earthquakes for rock sites and for soil sites at distances greater than 50 km . Differences in the standard deviations of up to 0.2 natural log units for moderate magnitudes at all distances and for large magnitudes at short distances result from the treatment of the magnitude dependence and the effects of nonlinear site response on the standard deviation.

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)0.010
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: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.014
GPT teacher head0.198
Teacher spread0.184 · 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

Citations310
Published2008
Admission routes1
Has abstractyes

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