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Record W2031883406 · doi:10.1785/0120120187

Crustal Shear-Wave Velocity Models Retrieved from Rayleigh-Wave Dispersion Data in Northeastern North America

2013· article· en· W2031883406 on OpenAlexafffundabout
Dariush Motazedian, Shutian Ma, Stephen A. Crane

Bibliographic record

VenueBulletin of the Seismological Society of America · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à Montréal
KeywordsGeologyRayleigh waveDispersion (optics)SeismologyWave velocityShear (geology)Surface waveRayleigh scatteringGeodesyGeophysicsPhysicsOpticsPaleontology

Abstract

fetched live from OpenAlex

On 23 June 2010, a moderate earthquake with Mw 5.2 occurred near the town of Val-des-Bois, Quebec, Canada, ∼60 km northeast of Ottawa, Ontario. The earthquake generated excellent crustal Rayleigh-wave records. We divided the 54 seismic stations that recorded clear Rayleigh-wave trains into 14 groups by station azimuth. In each group, we measured the Rayleigh-wave dispersion data station by station and formed one dispersion data file for the inversion. In this way, we ob- tained 14 crustal velocity models around the epicenter. We compared all 14 models and found that there are low-velocity layers in the top 10 km on the north side of the Ottawa-Bonnechere graben. Based on model similarity, we formed one model for the north side by averaging the north-side models and another model for the south side by averaging the south-side models. The separation of the north-side and south-side mod- els appears to follow the Ottawa-Bonnechere graben. In the top 10 km, the velocities in the south model are obviously slower than those in the north model.

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 score0.176
Threshold uncertainty score0.355

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.035
GPT teacher head0.202
Teacher spread0.167 · 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

Citations12
Published2013
Admission routes3
Has abstractyes

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Same venueBulletin of the Seismological Society of AmericaSame topicSeismic Waves and AnalysisFrench-language works237,207