MétaCan
Menu
Back to cohort
Record W6920515283 · doi:10.60692/gw2kv-47t09

Paleoseismological Findings at a New Trench Indicate the 1714 M8.1 Earthquake Ruptured the Main Frontal Thrust Over all the Bhutan Himalaya

2021· article· en· W6920515283 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2021
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods and Bayesian Inference
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTrenchForeland basinSlip (aerodynamics)Thrust faultSlow earthquakeSeismic gapAftershockEpicenter

Abstract

fetched live from OpenAlex

The 1714 Bhutan earthquake was one of the largest in the Himalaya in the last millennium. We show that the surface rupture caused by this earthquake extended further to the east than previously known, it was at least 175 km long, with slip exceeding 11 m at our study site. The age of the surface rupture was constrained by a combination of radiocarbon and traditional optically stimulated luminescence dating of affected river sediments. Computations using empirical scaling relationships, fitting historical observations and paleoseismic data, yielded a plausible magnitude of Mw 8.1 ± 0.4 and placed the hypocentre of the 1714 Bhutan earthquake on the flat segment of the Main Himalayan Thrust (MHT), the basal décollement of the Himalayan orogen. Calculations of Coulomb stress transfer indicate that great earthquakes along the leading part of the MHT would cause surface rupture. In contrast, distal earthquakes may not immediately trigger surface rupture, although they would increase the stresses in the leading part of the MHT, facilitating future surface-rupturing earthquakes. Frontal earthquakes would also transfer stress into the modern foreland basin facilitating southward propagation of the MHT as a blind basal décollement. In conclusion, studies of surface-rupturing events alone likely underestimate the seismic slip along the Himalayan megathrust.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.076
GPT teacher head0.285
Teacher spread0.210 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueGreater South Information SystemSame topicStatistical Methods and Bayesian InferenceFrench-language works237,207