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Distribution of siderophile elements during sulphide fractionation within magmatic Ni-Cu-PGE systems: A LA-ICP-MS investigation of the Crystal Lake Intrusion, 1.1 Ga Midcontinent rift

2022· article· en· W4286784875 on OpenAlexaff
Jennifer Smith, Wouter Bleeker, Duane C. Petts

Bibliographic record

VenueOre Geology Reviews · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsGeologyPentlanditeTrace elementGeochemistryPyrrhotitePetrographyChalcopyriteMineralogyPyriteCopperChemistry

Abstract

fetched live from OpenAlex

Within the magmatic pyrrhotite-pentlandite-chalcopyrite assemblage, which is ubiquitous of many Ni-Cu-PGE deposits, the spatial distribution of PGEs and semi-metals during both sulphide fractionation and low temperature alteration is not well documented. In-situ characterisation of sulphides provides a mechanism to trace the behaviour of elements during ore formation, with trace element distribution patterns providing valuable insights at the microscale into the genesis of magmatic sulphide deposits. Here we report trace element data determined by quantitative LA-ICP-MS mapping for a suite of variably altered samples from the taxitic rocks of the undeformed Crystal Lake Intrusion, within the 1.1 Ga Midcontinent Rift. Combining LA-ICP-MS data with detailed petrography and multivariate statistics helps identity the processes responsible for elevated metal concentrations and observed trace element distribution patterns within Ni-Cu-PGE systems. Such data can also improve our understanding of the effects fluid interaction and assimilation of volatile rich rocks has on the metal contents of sulphides. Element maps reveal complex distribution patterns throughout the disseminated sulphides of the Crystal Lake Intrusion, which are notably enriched in Pd relative to the massive sulphides developed locally along the basal contact. Replacement of the primary sulphides by low temperature phases is restricted to the northern limb of the intrusion and results in heterogeneous distributions and elevated concentrations of As, Pd, Ag and Bi along fractures. Zonation of As, Mo, Ru, Rh, Re and Bi is observed in both pentlandite and chalcopyrite. Within the globular ores, petrographic observations suggest the pyrrhotite-chalcopyrite contact with which ‘contact’ pentlandite resides along, may not be indicative of the initial boundary between monosulphide solid solution (MSS) and the residual sulphide liquid. This has implications for the interpretation of trace element zonation patterns, which in the case of this study are not considered to be indicative of formation by peritectic reaction. This is further supported by the spatial distribution of multivariate clusters which show features in the granular and contact pentlandite that are consistent with exsolution from MSS. Within the locally vapour saturated taxites of the southern limb, a new microfabric is documented within the globular ores, revealing a close spatial relationship between enhanced trace element concentrations and microstructures within undeformed magmatic sulphides. The microfabric, which is defined by V, Cr, As, Pb and Bi and less frequently by Mo, Rh, Pd and Re, is prevalent in unaltered coarse pyrrhotite and in contact, granular and fan textured pentlandite, and is developed in association with both silicate-capped and uncapped sulphide globules. Regardless of the exact mechanism by which the microfabric formed (e.g., fluid interaction, exsolution, gravitational compaction), this study highlights that regional deformation is not required to drive the preferential enrichment of select elements along discrete planes. Only as more high resolution element map data become available will it become apparent if the microfabric is a common feature of magmatic Ni-Cu-PGE deposits or a unique characteristic of the Crystal Lake Intrusion.

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 score0.010
Threshold uncertainty score0.997

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)0.0040.000

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 head0.202
Teacher spread0.189 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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