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Record W4327713835 · doi:10.1038/s43247-023-00722-8

Tetracarbonates in silicate melts may be at the origin of a deep carbon reservoir in the deep Earth

2023· article· en· W4327713835 on OpenAlexaff
Valerio Cerantola, Christoph J. Sahle, Sylvain Petitgirard, Min Wu, Stefano Checchia, Christopher Weis, Marco Di Michiel, G. Vaughan, Ines E. Collings, Róbert Arató, Max Wilke, Adrian Jones, Michael Hanfland, John S. Tse

Bibliographic record

VenueCommunications Earth & Environment · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsUniversity of Saskatchewan
FundersMinistero dell’Istruzione, dell’Università e della RicercaUniversità degli Studi di Milano-BicoccaNational Natural Science Foundation of ChinaEuropean Synchrotron Radiation FacilityDipartimenti di Eccellenza
KeywordsMantle (geology)SilicateCarbonateGeologyCarbon fibersMineralogyCarbon cycleOxygenGeochemistryMaterials scienceChemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Much of Earth’s carbon may have been stripped away from the silicate mantle by dense metallic-iron during core formation. However, at deep magma ocean conditions carbon becomes less siderophile and thus large amounts of it may be stranded instead in the deep mantle. Here, we describe the structure and compaction mechanisms of carbonate glass to deep mantle pressures. Our results, based on non-resonant inelastic X-ray scattering, X-ray diffraction and ab initio calculations, demonstrate a pressure-induced change in hybridization of carbon from sp 2 to sp 3 starting at 40 GPa, due to the conversion of [3] CO 3 2- groups into [4] CO 4 4- units, which is completed at ~112 GPa. The pressure-induced change of carbon coordination number from three to four increases possibilities for carbon-oxygen interactions with lower mantle silicate melts. sp 3 hybridized carbon provides a mechanism for changing the presumed siderophile nature of deep carbon, becoming a possible source for carbon-rich emissions registered at the surface in intra-plate and near-ridge hot spots.

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)0.001
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 score0.185
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.033
GPT teacher head0.245
Teacher spread0.213 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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