MétaCan
Menu
Back to cohort
Record W4404651090 · doi:10.1029/2024gl110058

Localization and Delocalization During Seismic Slip

2024· article· en· W4404651090 on OpenAlexaff
H. M. Savage, C. D. Rowe

Bibliographic record

VenueGeophysical Research Letters · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsMcGill University
FundersDivision of Earth Sciences
KeywordsGeologySeismologySlip (aerodynamics)GeophysicsEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Abstract The thickness of a seismic slip layer controls style and rate of rupture propagation, frictional heating, weakening, and energy budget of earthquakes. Slip layer thickness changes dynamically with feedbacks between temperature rise, roughness, damage, and fluid pressurization. Natural faults have complex slip histories, ambiguating which layer thicknesses represent a record of seismic slip. The thickness of proven paleoseismic slip layers in nature are 1 mm–1 cm. Thicker slip layers do not get hot enough to retain coseismic frictional temperature anomalies and thereby prevent the detection of big earthquakes using current methods. We suggest that delocalization—the spontaneous increase in slipping layer thickness during slip—plays a role in coseismic healing and cause biases in the rock record of earthquakes. Recognizing the importance of feedbacks affecting slipping layer thickness is critical to understanding strength and stress variations during earthquakes and to correctly interpreting earthquake source parameters from exhumed faults.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.027
GPT teacher head0.278
Teacher spread0.251 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicearthquake and tectonic studiesFrench-language works237,207