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Record W4229946712 · doi:10.1002/cpe.1520

Large‐scale numerical simulations of earthquake fault systems: illuminating the role of dilatational gravity in earthquake nucleation

2009· article· en· W4229946712 on OpenAlexafffund
T. Hayes, K. F. Tiampo, John B. Rundle

Bibliographic record

VenueConcurrency and Computation Practice and Experience · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsWestern UniversityTD Bank Group
FundersBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaInstitute for Catastrophic Loss ReductionNational Aeronautics and Space Administration
KeywordsSeismologyGeologyForeshockShear (geology)NucleationEarthquake simulationSlip (aerodynamics)TectonicsEarthquake predictionPhysicsAftershockPetrology

Abstract

fetched live from OpenAlex

Abstract Simulated slip histories and the associated gravity changes were generated using a large‐scale numerical simulation program for the San Andreas earthquake faults system, Virtual California. Statistical analysis of 55 000 years of these simulated earthquake cycles was investigated in order to study the usefulness of the dilatational gravity signal, those gravity changes due to a subsurface dilatational (or compressional) process, as a proxy for precursory stress and strain changes. Results indicate that the precursory dilatational gravity signal is dependent upon the fault orientation with respect to the tectonic loading plate velocity. This effect is interpreted as a consequence of preferential amplification of the shear stress or reduction of the normal stress, depending on the steady‐state regime investigated. Copyright © 2009 John Wiley & Sons, Ltd.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.279

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 communication0.0000.001
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 head0.014
GPT teacher head0.279
Teacher spread0.265 · 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 designObservational
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

Citations2
Published2009
Admission routes2
Has abstractyes

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