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Record W2111002935 · doi:10.1002/grl.50374

Thermal structure and megathrust seismogenic potential of the Makran subduction zone

2013· article· en· W2111002935 on OpenAlexaff
G. L. Smith, L.C. McNeill, Kelin Wang, Jiangheng He, T. Henstock

Bibliographic record

VenueGeophysical Research Letters · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsGeological Survey of Canada
FundersNatural Environment Research CouncilSight Research UK
KeywordsSubductionGeologySeismologyInduced seismicityEpisodic tremor and slipTsunami earthquakeTectonics

Abstract

fetched live from OpenAlex

The Makran subduction zone experienced a tsunamigenic Mw 8.1 earthquake in 1945 and recent, smaller earthquakes also suggest seismicity on the megathrust; however, its historical record is limited and hazard potential enigmatic. We have developed a 2‐D thermal model of the subduction zone. The results are twofold: (1) The thick sediment cover on the incoming plate leads to high (~150°) plate boundary temperatures at the deformation front making the megathrust potentially seismogenic to a shallow depth, and (2) the shallow dip of the subducting plate leads to a wide potential seismogenic zone (up to ~350 km). Combining these results with along strike rupture scenarios indicates that Mw8.7–9.2 earthquakes are possible in the seaward Makran subduction zone. These results have important earthquake and tsunami hazard implications, particularly for the adjacent coastlines of Pakistan, Iran, Oman, and India, as the Makran has not been previously considered a likely candidate for a Mw > 9 earthquake.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

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 communication0.0010.000
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 head0.016
GPT teacher head0.236
Teacher spread0.219 · 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

Citations138
Published2013
Admission routes1
Has abstractyes

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