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Record W2899619037 · doi:10.1029/2018gl079382

Earthquake Stress Drop in the Charlevoix Seismic Zone, Eastern Canada

2018· article· en· W2899619037 on OpenAlexafffundabout
John Onwuemeka, Yajing Liu, R. M. Harrington

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologies
KeywordsIntraplate earthquakeGeologySeismologyInduced seismicityCrustScalingDrop (telecommunication)GeophysicsTectonicsGeometry

Abstract

fetched live from OpenAlex

Abstract Stress drop scaling relations of earthquakes in intraplate seismic zones are less well constrained, partly due to less dense instrumentation and lower seismicity rate. Here we use new data to estimate the static stress drop values of earthquakes in the Charlevoix Seismic Zone in eastern Canada (MN < 4.5), June 2012 to July 2017. We first perform double‐difference relocation to obtain the hypocentral distribution of 518 events, which highlights a diffuse distribution within the Charlevoix Seismic Zone, probably related to the highly fractured crust by a Devonian meteorite impact. Using spectral ratios, we obtain stress drop values of 47 events ranging from ~2 to 200 MPa, typical of intraplate earthquakes, and observe an invariant stress drop scaling in the magnitude range Mw 2.2–3.8. Events within the impact structure have higher stress drops than those outside, which may indicate differences of fault maturity of the St. Lawrence paleorift system and the presence of a distributed fracture network.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.032
GPT teacher head0.269
Teacher spread0.237 · 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

Citations29
Published2018
Admission routes3
Has abstractyes

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