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Record W3097621006 · doi:10.1029/2021gc010092

How Fault Rocks Form and Evolve in the Shallow San Andreas Fault

2021· article· en· W3097621006 on OpenAlexaff
Randolph T. Williams, C. D. Rowe, Kristina Okamoto, H. M. Savage, Erin Eves

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsMcGill University
FundersSouthern California Earthquake Center
KeywordsGeologyCataclastic rockAuthigenicProtolithFault (geology)Fault gougeSeismologySlip (aerodynamics)Fault scarpPetrologyGeochemistryTectonicsSedimentary rock

Abstract

fetched live from OpenAlex

Abstract We document the mechanical and geochemical processes of fault rock development in the shallow San Andreas fault (Mojave segment), and quantify their importance in shaping the mineralogy, grain size, fabric, and frictional characteristics of gouge. Through a combination of field and laboratory analysis of an extensive suite of shallow (<150 m) drill cores, we show that fault rocks evolved from a granodiorite protolith via three main processes: distributed microfracturing/pulverization; cataclastic flow and incipient fabric development; and production of authigenic illite/smectite during fluid‐rock interaction. The interdependence of these mechanical and geochemical processes results in a diverse suite of fault rocks, and causes significant changes in frictional strength. Spatial variations in the effects of these mechanisms, as manifested in fault‐rock mineralogy and geochemistry, indicate marked variations in their relative contribution to fault‐rock evolution. These data reveal a complex San Andreas fault with multiple principal slip zones and damaged and altered rock hosting numerous interconnected secondary slip surfaces. The resulting picture of the San Andreas Fault zone suggests a substantial departure from the simple structures envisioned for near‐surface seismogenenic faults in numerical models is required, and may inform future efforts to forecast peak ground accelerations during southern California earthquakes.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.742

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 head0.011
GPT teacher head0.197
Teacher spread0.185 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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