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Record W4407406178 · doi:10.1130/g52248.1

Chemical and isotopic fractionation during melt inclusion formation

2025· article· en· W4407406178 on OpenAlexaff
Bruna da Silva Ricardo, Martin Oeser, Kendra J. Lynn, Neil Bennett, Xu Chu, Grant S. Henderson, Yanan Liu, Stefan Weyer, Corliss Kin I Sio

Bibliographic record

VenueGeology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFractionationGeologyInclusion (mineral)Equilibrium fractionationGeochemistryMelt inclusionsMineralogyIsotope fractionationChromatographyChemistryMantle (geology)

Abstract

fetched live from OpenAlex

Abstract Melt inclusions are used to study the origin and evolution of magmas. The extent to which they represent equilibrium melt compositions, however, critically hinges on the ratio of crystal growth rate to diffusion rate in melts. If the rate of crystal growth is limited by the supply of nutrients and the dissipation of unwanted components, the trapped melt will be depleted in compatible elements and enriched in incompatible elements. Despite widespread recognition of the potential for melt inclusions to trap disequilibrium compositions, their identification remains a challenge. Here, we demonstrate how stable isotopes provide a solution to this problem. Melt inclusions in basaltic tephra from Kīlauea volcano, Hawai‘i, display Mg isotope fractionation up to 0.49‰ (26Mg/24Mg) relative to bulk rock. These observations indicate that kinetic processes, such as chemical diffusion, are at play, as equilibrium mineral-melt isotope fractionation at high temperatures is an order of magnitude smaller. We propose that the heavy Mg isotopic compositions of the melt inclusions were generated by the faster diffusion and thus preferential incorporation of 24Mg by the growing olivine, leaving the slower diffusant (26Mg) behind. The incompatible elements (e.g., Ca, Ti, and K) are predicted to display similarly large fractionations when boundary layer effects are significant. These findings show that stable isotopes can be a useful tool in identifying disequilibrium compositions in melt inclusions.

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 score0.447
Threshold uncertainty score0.124

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 head0.003
GPT teacher head0.220
Teacher spread0.217 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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