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Record W4395450538 · doi:10.1038/s41467-024-47887-0

Seismic evidence for melt-rich lithosphere-asthenosphere boundary beneath young slab at Cascadia

2024· article· en· W4395450538 on OpenAlexaff
Xin Wang, Ling Chen, Kelin Wang, Qi‐Fu Chen, Zhongwen Zhan, Jianfeng Yang

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsGeological Survey of Canada
FundersInstitute of Geology and Geophysics, Chinese Academy of SciencesNational Natural Science Foundation of ChinaChinese Academy of SciencesNational Science Foundation
KeywordsSubductionAsthenosphereGeologyLithosphereSlabMantle (geology)SeismologySlab windowGeophysicsOceanic crustTectonics

Abstract

fetched live from OpenAlex

Abstract The Lithosphere-Asthenosphere Boundary (LAB) beneath oceanic plates is generally imaged as a sharp seismic velocity reduction, suggesting the presence of partial melts. However, the fate of a melt-rich LAB is unclear after these plates descend into the mantle at subduction zones. Recent geophysical studies suggest its persistence with down-going old and cold slabs, but whether or not it is commonly present remains unclear, especially for young and warm slabs such as in the Cascadia subduction zone. Here we provide evidence for its presence at Cascadia in the form of a large (9.8 $$\pm$$ ± 1.5 $$\%$$ % ) decrease in shear-wave velocity over a very small (<3 km) depth interval. Similarly large and sharp seismic velocity reduction at the bottom of both old and young slabs, as well as along the base of oceanic plates before subduction, possibly represents widespread presence of melts. The melt-rich sub-slab LAB may strongly influence subduction dynamics and viscoelastic earthquake cycles.

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 score0.068
Threshold uncertainty score0.135

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.296
Teacher spread0.267 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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