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Record W4412951396 · doi:10.1016/j.lithos.2025.108205

Diamond – garnet elastic geobarometry applied to an eclogitic system

2025· article· en· W4412951396 on OpenAlexfundno aff
Marta Morana, Lorenzo Barni, Fang Hu, Giulia Marras, Gianfelice Cinque, Antonio Angellotti, Fabrizio Nestola, A. M. Logvinova, Д. С. Михайленко, Luca Bindi, Vincenzo Stagno

Bibliographic record

VenueLithos · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
FundersUral Branch, Russian Academy of SciencesMinistero dell'Università e della RicercaEuropean Synchrotron Radiation FacilityCanadian Anesthesiologists' Society
KeywordsGeologyGeochemistryDiamondMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

The investigation of mineral inclusions in diamonds represents a unique tool to better understand the mineralogy and composition of hidden portions of Earth's mantle and, hence, determine conditions of pressure and temperature at the time of diamond formation. Using a combination of experimental techniques and different geothermobarometric approaches, we characterized a natural diamond from Udachnaya kimberlite pipe entrapping nine inclusions; the inclusions are five garnets, three clinopyroxene and one sulfide and represent an eclogitic paragenesis. Here, we adopted, for the first time, the elastic geobarometry method to the garnet-diamond inclusion-host system to calculate the entrapment conditions for the diamond-garnet pair, resulting in 5.7(±0.3) GPa at 1154 °C. These P-T data are compared with estimates obtained through chemical geothermobarometry, employing T projection onto the local geotherm, a common approach used for eclogite xenoliths in absence of robust calibrated barometers. Our data demonstrate that elastic geobarometry for the garnet-diamond pair results to be a very reliable tool to determine the diamond formation also for eclogitic systems and this will allow to expand our knowledge on eclogitic diamonds in terms of depth of formation.

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 score0.062
Threshold uncertainty score0.837

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.207
Teacher spread0.200 · 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 routes1
Has abstractyes

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