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Record W2970891172 · doi:10.1130/b35180.1

A genetic link between iron oxide-apatite and iron skarn mineralization in the Jinniu volcanic basin, Daye district, eastern China: Evidence from magnetite geochemistry and multi-mineral U-Pb geochronology

2019· article· en· W2970891172 on OpenAlexaff
Hao Hu, Jianwei Li, Daniel E. Harlov, David R. Lentz, Christopher R.M. McFarlane, Yue‐Heng Yang

Bibliographic record

VenueGeological Society of America Bulletin · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsSkarnGeologyGeochemistryIron oxide copper gold ore depositsOre genesisMagnetiteMineralization (soil science)ActinoliteDioriteMagmatic waterGenetic modelZirconEpidoteHydrothermal circulationMeteoric waterFluid inclusionsChloriteQuartz

Abstract

fetched live from OpenAlex

Abstract Various styles of ore deposits may form from a single magmatic-hydrothermal system. Identification of a possible genetic link between different ore types in a region is not only of critical importance for a better understanding of the magmatic-hydrothermal processes, but can also help in successful mineral exploration. Both iron oxide-apatite (IOA) and iron skarn deposits are closely associated with intrusive rocks of intermediate to felsic in composition, but whether these two ore types can form from the same magmatic intrusion remains poorly understood. In this study, we present a comparative study between a newly identified subsurface IOA ore body located at the apex of a diorite porphyry and the iron skarn ore bodies located immediately above it in the Jinniu volcanic basin of the Daye district, Middle-Lower Yangtze River metallogenetic belt (MLYRMB), eastern China in order to highlight a genetic link between these two styles of mineralization. The IOA ores are dominated by Ti-rich magnetite with variable amounts of fluorapatite, diopside, and actinolite. This mineralogical assemblage is distinctly different from the iron skarn ores, which consist mainly of Ti-depleted magnetite and subordinate pre-ore garnet and diopside, and post-ore quartz, chlorite, calcite, and pyrite. In addition, magnetite from the IOA ores is characterized by well-developed ilmenite lamellae and has high concentrations of Ni, V, Co, and Ga, consistent with high temperature crystallization, whereas magnetite grains from the iron skarn ores usually exhibit oscillatory growth zones and contain much lower Ni, V, Co, and Ga, indicating their formation under relatively low temperatures. Titanite and fluorapatite from the IOA ores have U-Pb ages of 132.5 ± 2.4 Ma to 128.4 ± 3.0 Ma, which match a titanite U-Pb age for the associated iron skarn ores (132.3 ± 2.0 Ma), and are consistent with zircon U-Pb ages for the ore-hosting diorite porphyry (130.4 ± 0.7 Ma to 130.3 ± 0.5 Ma). This age consistency supports a possible genetic link among the diorite porphyry, IOA ores, and iron skarn ores. We propose that the IOA and skarn ores are the products of two consecutive mineralization stages of the same magmatic-hydrothermal system, involving a high-temperature, hypersaline fluid coexisting with the diorite porphyry magma during emplacement and a subsequent low temperature, diluted hydrothermal fluid. Other IOA and iron skarn deposits of similar ages (130 Ma) are found in a series of volcanic basins in the MLYRMB, which forms one of the world’s largest IOA metallogenic belts. The close association of the two ore styles identified at Daye provides a useful exploration guide for IOA and iron skarn deposits both on a local and regional scale.

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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)0.000
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 score0.037
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.010
GPT teacher head0.195
Teacher spread0.185 · 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

Citations53
Published2019
Admission routes1
Has abstractyes

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