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Record W4402232958 · doi:10.1029/2024jb029104

Strong Lg‐Wave Attenuation Reveals Quarter‐Toroidal Crustal Melting Around the Yakutat Terrane in South‐Central Alaska

2024· article· en· W4402232958 on OpenAlexaboutno aff
Geng Yang, Lian‐Feng Zhao, Xiao‐Bi Xie, Xi He, Lei Zhang, Zhenxing Yao

Bibliographic record

VenueJournal of Geophysical Research Solid Earth · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsTerraneAttenuationQuarter (Canadian coin)SeismologyGeologyToroidPhysicsGeographyTectonicsNuclear physicsOpticsArchaeology

Abstract

fetched live from OpenAlex

Abstract South‐central Alaska features a history of massive volcanic activity. How the Denali volcanic gap (DVG) formed and why the Wrangell volcanoes are clustered remain vigorously debated. Investigating the crustal thermal structure can be crucial for understanding subsurface magmatic activity. We present a high‐resolution broadband Lg‐wave attenuation model to constrain crustal thermal anomalies beneath Alaska. Strong Lg attenuation is observed beneath the volcanoes in south‐central Alaska, indicating thermal anomalies and possible melting in the crust. In contrast, the central Yakutat terrane (YT) and DVG are characterized by weak Lg attenuation, suggesting the existence of a cool crust that prevents hot mantle materials from invading the crust. This cool crust is likely the reason for the DVG. Quarter‐toroidal crustal melting with strong attenuation is revealed around the YT. This curved zone of crustal melting, possibly driven by toroidal mantle flow, weakly connects the Wrangell and Buzzard Creek‐Jumbo Dome magmatic chambers.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.070

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 communication0.0000.000
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 head0.059
GPT teacher head0.325
Teacher spread0.266 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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