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Record W2118703264 · doi:10.1785/0120130265

Crustal Shear-Wave Velocity Models Retrieved from Rayleigh-Wave Dispersion Data in Northern Canada

2014· article· en· W2118703264 on OpenAlexaffabout
Dariush Motazedian, Shutian Ma

Bibliographic record

VenueBulletin of the Seismological Society of America · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsGeologyRayleigh waveSeismologyDispersion (optics)Shear (geology)Wave velocitySurface waveLove waveGeodesyGeophysicsWave propagationLongitudinal wavePhysicsPetrologyOpticsMechanical wave

Abstract

fetched live from OpenAlex

Abstract Baffin Island is one of the several seismically active regions in the far north of Canada. In 1933, a strong earthquake withMw 7.3 occurred in the region. On 7 July 2009, a relatively strong earthquake with Mw 6.0 occurred in the same area. This earthquake was very well recorded by many modern seismic stations. We sys-tematically organized the Rayleigh-wave displacement records, measured Rayleigh-wave dispersion data at 28 stations surrounding the epicenter, and retrieved the S-wave velocity models. We used a previous model for the Western Quebec seismic zone as the initial model in our analyses and found that the velocities of all models at the shallow depths (<15 km) were obviously slower than those of the initial model. In the middle crust, the velocities in most models were close to those of the initial model. In the directions of azimuths 171 ° ∼ 218 ° and 241°, the velocities in the middle crust were faster than those of the initial model. In the directions of 263 ° ∼ 279°, the veloc-ities in the middle crust were slower than those of the initial model. The slowest S-wave velocities in the top layers occurred in the directions of azimuths 90 ° and 218°. These findings indicate differences in the existing crustal structures.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.027
GPT teacher head0.190
Teacher spread0.163 · 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.

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

Citations8
Published2014
Admission routes2
Has abstractyes

Explore more

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