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Record W2520303659 · doi:10.1785/0120160049

Site‐Effects Model for Central and Eastern North America Based on Peak Frequency

2016· article· en· W2520303659 on OpenAlexaff
Behzad Hassani, Gail M. Atkinson

Bibliographic record

VenueBulletin of the Seismological Society of America · 2016
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsGeologyGeographyEnvironmental science

Abstract

fetched live from OpenAlex

Abstract We develop a regional site‐effects model for central and eastern North America based on an analysis of the residuals of observed ground‐motion parameters relative to two regional ground‐motion prediction equations: one model has a hard‐rock (site class A) reference site condition, whereas the other is referenced to a B/C boundary site condition (site classification of National Earthquake Hazard Reduction Program). In both cases, the residuals are well described by a site‐effects model based on site fundamental frequency f peak , in which f peak is as determined from the horizontal‐to‐vertical component response spectral ratios. Accordingly, we derive an f peak ‐based site amplification model with respect to B/C and hard‐rock reference site conditions. Implementing the f peak ‐based model, we reduce random variability in amplitudes σ by 10% on average, for a selected database from the Next Generation Attenuation‐East Project, relative to the value obtained when using a generic site‐effects model parameterized by near‐surface shear‐wave velocity (time‐averaged shear‐wave velocity in the upper 30 m, V S 30 ). The reduction in σ comes from the site‐to‐site component of the variability (reduced by 20% on average), whereas the single‐station variability is unaffected.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.408
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.190
Teacher spread0.182 · 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 teacher head, 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

Citations57
Published2016
Admission routes1
Has abstractyes

Explore more

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