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Using combined texture-element-isotope indicators of sulfides to trace fluid mixing and evolution in Paleozoic IOCG system

2024· article· en· W4403014715 on OpenAlexaff
Shuanliang Zhang, Georges Beaudoin, Liandang Zhao, Lin Gong, Weipin Sun, Bing Xiao

Bibliographic record

VenueOre Geology Reviews · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversité Laval
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsIron oxide copper gold ore depositsGeologyPaleozoicGeochemistryTrace elementTexture (cosmology)Mixing (physics)Earth sciencePetrologyPaleontologyFluid inclusionsQuartz

Abstract

fetched live from OpenAlex

Geochemistry of sulfides is widely used to constrain sources and ore-forming processes of various mineral deposits. However, its application on Fe–oxide Cu–Au (IOCG) deposits is not well constrained due to multiphase pyrite and Cu minerals and their complex inheritance relationships. The Shuanglong deposit is an IOCG-like deposit in the Eastern Tianshan characterized by abundant Py1 and Py2 in the Fe mineralization stage (II) and Py3 and Py4 in the Cu mineralization stage (III). Chalcopyrite is divided into three types where Ccp1 is formed by replacing Py1 and Py2, Ccp2 coexists with Py4, and higher grade Ccp3 is in quartz–hematite–chalcopyrite veins without pyrite. Py1 and Py2 are associated with multiphase magnetite in stage II Fe mineralization, whereas Py3 coexists with the early epidote replaced by the late calcite–hematite–Py4–Ccp2 assemblage in stage III Cu mineralization. The increasing Co contents and Co/Ni ratios and decreasing δ34S values (∼8‰ to 4 ‰) from core to rim in Py1 and Py2 indicate temperature and oxygen fugacity increases during the input of magmatic-hydrothermal fluids. Decreasing Co/Ni, increasing Au, and δ 34 S fluid (from ∼ 6 ‰ to 30 ‰) from Py3 to Py4 show fluid mixing between the magmatic-hydrothermal fluid and oxidized non-magmatic sulfur such as seawater or basinal brine sulfate during the stage III Cu mineralization. Ccp1 inherited its sulfur and certain trace elements such as Pb and Zn from pyrite, whereas the addition of external sulfur contributed to local high-grade Cu mineralization. Such external sulfur may be from the seawater or basinal brine sulfate, with an increasing contribution during precipitation of Ccp2 and Ccp3 shown by fluid δ 34 S values (from 26 ‰ to 36 ‰). Combined with the decreasing Cd/Zn ratios from Ccp1 to Ccp3 caused by increasing total sulfur in fluids, this suggests that the sulfate sulfur contribution may have played a significant role in the high-grade Cu mineralization in IOCG deposits. This study also highlights the importance of the detailed texture of pyrite using acid etching, coupled with in-situ sulfur isotope and trace elements of pyrite and chalcopyrite for IOCG deposit research.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.244
Teacher spread0.225 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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