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Record W4284686217 · doi:10.1093/petrology/egac062

Compositional Variations in Primitive Kimberlite Melts and Entrained Mantle Cargo from a Global Survey of Trace Element Compositions in Kimberlite Olivine

2022· article· en· W4284686217 on OpenAlexaboutno aff
Geoffrey H. Howarth, Andrea Giuliani, Ashton Soltys, Yannick Bussweiler

Bibliographic record

VenueJournal of Petrology · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsKimberliteOlivineGeologyMantle (geology)PeridotiteTrace elementGeochemistryIncompatible elementPartial meltingMineralogy

Abstract

fetched live from OpenAlex

Abstract Olivines are the dominant phase of kimberlites and the majority of grains display distinct compositional zoning with xenocrystic cores surrounded by magmatic rims. Previous work has documented large variations in both core and rim major and minor element compositions in kimberlites globally, which has been related to variable entrainment and assimilation of sub-continental lithospheric mantle (SCLM) material. However, there is limited knowledge of trace element variations in olivine from kimberlites and it is unclear whether mantle assimilation has any effect on the trace element composition of kimberlite melts. To fill this gap, we present a global survey of olivine trace element compositions, along with previously reported major and minor element compositions, for samples representing the full spectrum of olivine compositional variations in kimberlites, including samples from the following: Lac de Gras, Canada (Grizzly, Koala); Kimberley, South Africa (Bultfontein, De Beers, Kimberley Big Hole, Wesselton); Kaalvallei, South Africa (New Robinson, Samada); and Alto Paranaiba, Brazil (Limpeza-18, Tres Ranchos-04). Trace element concentrations of olivine cores can discriminate between those derived from the disaggregation of peridotitic material and those related to the megacryst suite. The megacrystic olivine cores exhibit a negative correlation between Al and Mn, which is absent in peridotite-derived cores, and are characterised by high concentrations of temperature-dependent elements (e.g. Al, Na, V) as well as Zn, Ti, and Mn. Following pre-screening of cores for megacrystic and spinel peridotite-derived grains, we applied the Al-in-olivine thermometer to assess the P–T equilibration conditions of cores in equilibrium with garnet and estimate the sampling depth of kimberlite magmas in the lithospheric mantle. Our results are consistent with predominant entrainment of deep lithosphere xenocrysts in highly diamondiferous compared with diamond-poor kimberlites. Temperature-dependent elements display a gradational increase with depth due to higher T with Ca, Cu and, to a lesser extent, Zn and Ti being higher and Mg# being lower towards the base of the SCLM, which is consistent with melt modification of the lower lithosphere. The Zn, Ti, Co, Mn, Li, Al, Cr, Na, and V concentrations of magmatic olivine rims display systematic variations that have a negative correlation with Mg# (whereas Cr is positively correlated). Lac de Gras olivine feature Mg-rich rims (Mg# >90) and low concentrations of these trace elements, whereas the Fe-rich olivine rims (Mg# ~85) of the Kaalvallei kimberlites have higher concentrations of these elements, with the Kimberley and Alto Paranaiba kimberlites being intermediate. Direct correlations between average Ti, Zn, Co and Li compositions of olivine cores and rims suggests that the olivine rim (i.e. proxy for primitive melt) variations are related to variable assimilation of metasomatised SCLM and can be effectively used to track the composition of the lithospheric column that is traversed by kimberlite magmas. These observations further imply an intimate link between early proto-kimberlite melt, leading to formation of megacrystic olivine at the base of the SCLM, and the composition of kimberlite melts which entrain and assimilate these products. We conclude that lithospheric mantle assimilation has a major and previously overlooked influence on the trace element composition of kimberlite magmas.

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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)0.001
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 score0.028
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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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

Citations32
Published2022
Admission routes1
Has abstractyes

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