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Record W3019256801 · doi:10.1126/sciadv.aaz0424

Kimberlite genesis from a common carbonate-rich primary melt modified by lithospheric mantle assimilation

2020· article· en· W3019256801 on OpenAlexaff
Andrea Giuliani, D. Graham Pearson, Ashton Soltys, Hayden Dalton, David Phillips, Stephen Foley, Emilie Lim, Karsten Goemann, William L. Griffin, Roger H. Mitchell

Bibliographic record

VenueScience Advances · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsLakehead UniversityUniversity of Alberta
FundersAustralian Research CouncilSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsKimberliteGeologyMantle (geology)CarbonateGeochemistryLithosphereEarth scienceCarbonatiteAssimilation (phonology)PetrologyPaleontologyTectonicsChemistry

Abstract

fetched live from OpenAlex

Quantifying the compositional evolution of mantle-derived melts from source to surface is fundamental for constraining the nature of primary melts and deep Earth composition. Despite abundant evidence for interaction between carbonate-rich melts, including diamondiferous kimberlites, and mantle wall rocks en route to surface, the effects of this interaction on melt compositions are poorly constrained. Here, we demonstrate a robust linear correlation between the Mg/Si ratios of kimberlites and their entrained mantle components and between Mg/Fe ratios of mantle-derived olivine cores and magmatic olivine rims in kimberlites worldwide. Combined with numerical modeling, these findings indicate that kimberlite melts with highly variable composition were broadly similar before lithosphere assimilation. This implies that kimberlites worldwide originated by partial melting of compositionally similar convective mantle sources under comparable physical conditions. We conclude that mantle assimilation markedly alters the major element composition of carbonate-rich melts and is a major process in the evolution of mantle-derived magmas.

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 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 score0.138
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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

Citations131
Published2020
Admission routes1
Has abstractyes

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