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Record W4410994067 · doi:10.5382/econgeo.5156

Major and Trace Element Concentrations in Chromite and Silicate Minerals of the Critical Zone of the Bushveld Complex, South Africa: Effects of Reequilibration and Crystal Fractionation on Chromite Composition

2025· article· en· W4410994067 on OpenAlexaff
Sarah‐Jane Barnes, Wolfgang Maier, Dany Savard, Stephen A. Prevec

Bibliographic record

VenueEconomic Geology · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsChromiteGeologySilicateGeochemistryFractionationTrace elementMineralogySilicate mineralsChemistry

Abstract

fetched live from OpenAlex

Abstract The Rustenburg Layered Suite of the Bushveld Complex, South Africa, contains the world’s largest resource of chrome and platinum group elements (PGEs). Both Cr and PGEs are found in chromitite layers within an approximately 1,000-m-thick section of ultramafic to mafic rocks known as the Critical zone. Neither the process of how the chromitite layers form nor the role that chromite plays in collecting the PGEs is clear. Major and trace element contents of chromite and silicate minerals from each of the 13 chromitite layers, and from chromite in the adjacent peridotites and norites, have been determined. The concentrations of PGEs in both chromite and silicates are less than detection levels (10–20 ppb). Thus, neither are the host of the PGEs in these rocks. The Cr# and Fe# of the chromites from chromitite layers are similar to those found in experiments carried out to model the crystallization of the initial magma (B1) of the Bushveld, with the same decrease in Cr# with increase in Fe#. The fO2 of the experiments Δ 0 FMQ (where FMQ = fayalite-magnetite-quartz buffer) and those of the chromitite chromite calculated from the Fe3+/FeTotal ratios and the V contents of the chromite are similar. Variations in trace element contents of the chromitite chromite can also be modeled using the B1 composition and allowing for ~40% crystal fractionation across the stratigraphy.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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