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Record W2888271949 · doi:10.1029/2018jb015768

Frictional Mechanics of Slow Earthquakes

2018· article· en· W2888271949 on OpenAlexaff
J. R. Leeman, Chris Marone, D. M. Saffer

Bibliographic record

VenueJournal of Geophysical Research Solid Earth · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsGeomechanica (Canada)
FundersPennsylvania State UniversityNational Science Foundation
KeywordsSlip (aerodynamics)MechanicsInstabilityGeologyRheologySlip line fieldEpisodic tremor and slipSeismologyPhysicsTectonicsSubductionShear (geology)Petrology

Abstract

fetched live from OpenAlex

Abstract Tectonic faults slip in a wide range of modes that span from slow slip events to dynamic rupture. A growing body of observations document this spectrum of failure modes in many geologic settings. However, the physical mechanisms that dictate slow slip are not understood. Here we investigate the mechanics of slow slip using carefully controlled laboratory experiments that demonstrate a complete spectrum of slip modes: Laboratory stick‐slip event durations span from seconds to milliseconds, representing the equivalent of failure events that span the range from slow to dynamic earthquakes. The rheological critical stiffness kc is the primary control on the mode of slip, but higher‐order effects including velocity dependence of the frictional rate parameter and critical slip distance also play an important role. We also find that quasi‐dynamic instability results from negligible stress drop near the stability boundary, in tandem with negative feedback during slip acceleration rooted in the rate dependence of kc. Our work shows that a broad spectrum of slip behaviors can arise from a common frictional mechanism modulated by fault zone rheology and elastic properties.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.052
GPT teacher head0.325
Teacher spread0.273 · 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 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

Citations100
Published2018
Admission routes1
Has abstractyes

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