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Skarn classification and element mobility in the Yeshan Iron Deposit, Eastern China: Insight from lithogeochemistry

2022· article· en· W4224436894 on OpenAlexaff
Shugao Zhao, Matthew J. Brzozowski, Thomas Mueller, Lijuan Wang, Weiqiang Li

Bibliographic record

VenueOre Geology Reviews · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsLakehead University
FundersNanjing UniversityNational Natural Science Foundation of China
KeywordsSkarnProtolithGeologyGeochemistryMetasomatismIgneous rockCarbonate rockPetrologyCarbonateMetamorphic rockPlutonMantle (geology)Sedimentary rockFluid inclusions

Abstract

fetched live from OpenAlex

Skarns form by significant fluid-mediated exchange of mass and heat between igneous rocks and their surrounding wall rocks into which they intruded. Quantification of the mass exchange associated with skarn alteration requires knowledge of the skarn protoliths, which are often masked by metamorphic recrystallization and intense calc-silicate metasomatism. To overcome this challenge in characterizing the Yeshan skarn Fe deposit in Eastern China, a cross-section through the complete rock sequence, from the carbonate wall rock to the pluton, was systematically sampled, and analyzed for bulk-rock major and trace elements. Underpinned by the skarn zonation model, ln(SiO2/Al2O3), ln(SiO2/TiO2), and REE + Y values in the skarns were used to distinguish the various skarn protoliths. The effectiveness of the ln(SiO2/Al2O3) and ln(SiO2/TiO2) is supported by the variable mobility of Si, Al, and Ti during magma-derived fluid infiltration into the carbonate wall rocks at Yeshan. The effectiveness of REE + Y is based on their significant concentration differences in the carbonate wall rocks and igneous rocks at Yeshan. These geochemical indexes may be applicable to the characterization of protoliths and mass transfer in skarn deposits where igneous rocks intruded carbonate wall rocks.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0080.000

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 head0.021
GPT teacher head0.221
Teacher spread0.200 · 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
Published2022
Admission routes1
Has abstractyes

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