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Record W4413411439 · doi:10.1093/petrology/egaf076

Experiments and Models Bearing on the Role of Magma Mixing and Contamination on Chromite Crystallization in Ultramafic Magmas

2025· article· en· W4413411439 on OpenAlexafffund
Erin Keltie, James M. Brenan

Bibliographic record

VenueJournal of Petrology · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsDalhousie University
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsChromiteUltramafic rockGeologyGeochemistryMagmaBearing (navigation)ContaminationCrystallizationPetrologyVolcanoChemical engineering

Abstract

fetched live from OpenAlex

Abstract To better understand the origin of stratiform chromitites, we experimentally evaluate the role of bulk compositional shifts arising from mixing or contamination of a mafic magma on both the liquid line of descent and the chromite saturation state. Experiments equilibrated synthetic Cr-bearing komatiite containing 0–50 wt % Cr-free contaminants (granodiorite (GD), Fe-rich shale (Fe-shale), metasediment (MS), natural magnetite (NM)) on Fe-pre-saturated Pt or Ir loops at 1192–1462 °C and 0.1 MPa at the fayalite-magnetite-quartz (FMQ) oxygen buffer. Experimental run products involving mixtures of komatiite with silicate contaminants produced some combination of euhedral olivine, chromite and glass, whereas initial cubes of NM nearly completely dissolved to produce Fe-enriched melt plus Cr-rich magnetite. The addition of contaminants results in the chromium content of the melt at chromite saturation (CCCS) to decrease with increasing melt FeO or decreasing melt SiO2 abundance. Assessment of the temperature dependence reveals that the log of the CCCS decreases linearly with inverse temperature, allowing for the chromite crystallization temperature to be predicted for a given melt Cr concentration. The addition of any of the studied contaminants decreases the modal abundance of olivine.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.150

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 head0.205
Teacher spread0.196 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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