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Record W2042816588 · doi:10.1038/ncomms6604

Partial melting of deeply subducted eclogite from the Sulu orogen in China

2014· article· en· W2042816588 on OpenAlexaff
Lu Wang, Timothy Kusky, Ali Polat, Songjie Wang, Xingfu Jiang, Keqing Zong, Junpeng Wang, Hao Deng, Jianmin Fu

Bibliographic record

VenueNature Communications · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Windsor
FundersFundamental Research Funds for the Central UniversitiesChina Postdoctoral Science FoundationNational Natural Science Foundation of ChinaRobert Bosch StiftungMinistry of Education of the People's Republic of ChinaNational Science Foundation
KeywordsEclogitePartial meltingGeologySubductionGeochemistryMagmatismCoesiteLithosphereMetamorphismMantle (geology)PetrologyZirconCrustTectonicsPaleontology

Abstract

fetched live from OpenAlex

We report partial melting of an ultrahigh pressure eclogite in the Mesozoic Sulu orogen, China. Eclogitic migmatite shows successive stages of initial intragranular and grain boundary melt droplets, which grow into a three-dimensional interconnected intergranular network, then segregate and accumulate in pressure shadow areas and then merge to form melt channels and dikes that transport magma to higher in the lithosphere. Here we show, using zircon U-Pb dating and petrological analyses, that partial melting occurred at 228-219 Myr ago, shortly after peak metamorphism at 230 Myr ago. The melts and residues are complimentarily enriched and depleted in light rare earth element (LREE) compared with the original rock. Partial melting of deeply subducted eclogite is an important process in determining the rheological structure and mechanical behaviour of subducted lithosphere and its rapid exhumation, controlling the flow of deep lithospheric material, and for generation of melts from the upper mantle, potentially contributing to arc magmatism and growth of continental crust.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.013
GPT teacher head0.225
Teacher spread0.213 · 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

Citations169
Published2014
Admission routes1
Has abstractyes

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