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Record W3111671128 · doi:10.1130/g48417.1

Cumulate olivine: A novel host for heavy rare earth element mineralization

2020· article· en· W3111671128 on OpenAlexaff
S. Brandt, Marc Lorin Fassbender, Reiner Klemd, Chandra Macauley, Peter Felfer, Karsten M. Haase

Bibliographic record

VenueGeology · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFayaliteOlivineGeochemistryGeologyMetasomatismFelsicMaficMineralogyRare-earth elementMineralization (soil science)Igneous rockPyroxeneMantle (geology)Rare earth

Abstract

fetched live from OpenAlex

Abstract Olivine is one of the most important minerals used to reconstruct magmatic processes, yet the rare earth element (REE) systematics of Fe-rich olivine in igneous rocks and ore deposits is poorly understood. As detected by in situ laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) analysis, cumulate fayalite (Fe2SiO4) in the Paleoproterozoic Vergenoeg F-Fe-REE deposit of the Bushveld large igneous province (LIP) in South Africa contains the highest heavy REE (HREE) contents ever recorded for olivine, with HREE enrichment of as much as 6000× chondritic values. Atom probe tomography maps confirm the incorporation of the HREEs into the fayalite crystal lattice, facilitated by lithium acting as a main charge balancer and by high REE contents in the highly fractionated felsic parental melt that is related to the Bushveld LIP. The high HREE concentrations of fayalite in concert with its high modal abundance (>95 vol%) indicate that the fayalite cumulates are the main host for the HREE mineralization of the Vergenoeg deposit. Fayalites of Vergenoeg demonstrate that Fe-rich olivine may fractionate large amounts of HREEs, and we propose fayalite cumulates as potential future targets for HREE exploration.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.025
GPT teacher head0.215
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2020
Admission routes1
Has abstractyes

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