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Record W2163689531 · doi:10.1130/g34901.1

Biomarkers heat up during earthquakes: New evidence of seismic slip in the rock record

2014· article· en· W2163689531 on OpenAlexaff
H. M. Savage, P. J. Polissar, Rachel Sheppard, C. D. Rowe, E. E. Brodsky

Bibliographic record

VenueGeology · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsMcGill University
FundersNational Science Foundation
KeywordsObservatoryROWEColumbia universityGeologyLibrary scienceArchaeologyArt historyHistoryMedia studiesSociologyComputer scienceManagementAstronomy

Abstract

fetched live from OpenAlex

During earthquakes, faults heat up due to frictional work. However, evidence of heating from paleoearthquakes along exhumed faults remains scarce. Here we describe a method using thermal maturation of organic molecules in sedimentary rock to determine whether a fault has experienced differential heating compared to surrounding rocks. We demonstrate the utility of this method on an ancient, pseudotachylyte-hosting megathrust at Pasagshak Point, Alaska. Measurements of the ratio of thermally stable to thermally unstable compounds (diamondoids/n-alkanes) show that the melt-bearing rocks have higher thermal maturity than surrounding rocks. Furthermore, the mineralogy of the survivor grains and the presence of any organic molecules allow us to constrain the temperature rise during the ancient earthquakes to 840–1170 °C above ambient temperatures of ∼260 °C. From this temperature rise, we estimate that the frictional work of the earthquake was ∼105–228 MJ/m2. Using experimental friction measurements as a constraint, we estimate that the minimum slip necessary for heating was ∼1–8 m. This paper demonstrates that biomarkers will be a useful tool to identify seismic slip along faults without frictional melt.

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 score0.045
Threshold uncertainty score0.989

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)0.0010.000

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 head0.024
GPT teacher head0.231
Teacher spread0.207 · 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

Citations74
Published2014
Admission routes1
Has abstractyes

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