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Record W3197773705 · doi:10.7185/geochemlet.2122

High pressure redistribution of nitrogen and sulfur during planetary stratification

2021· article· en· W3197773705 on OpenAlexaff
Chloe Jackson, Elizabeth Cottrell, Zhenyu Du, Neil Bennett, Yue Fei

Bibliographic record

VenueGeochemical Perspectives Letters · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversity of Toronto
FundersUniversity of ChicagoNational Museum of Natural HistoryTulane UniversityNuclear Safety and Security CommissionChinese Academy of SciencesCarnegie Institution of WashingtonOffice of ScienceNational Aeronautics and Space AdministrationArgonne National LaboratoryU.S. Department of EnergySmithsonian InstitutionNational Science Foundation
KeywordsRedistribution (election)SulfurNitrogenStratification (seeds)Environmental scienceChemistryMaterials sciencePolitical scienceMetallurgyBiologyLaw

Abstract

fetched live from OpenAlex

Nitrogen is essential to life, and yet is also the most depleted element in the Earth relative to gas-rich chondrites. A key expression of Earth's N depletion is its elevated sulfur-nitrogen (S/N) ratio. Primordial stratification into a core, mantle, and atmosphere is the largest mass transfer process that terrestrial planets experience, but the data required to evaluate how S/N ratios respond to primordial stratification of Earth-sized planets do not exist. We report new metal-silicate partitioning experiments on N up to 26 GPa and 3437 K. Our data indicate that nitrogen becomes more siderophile with increasing pressure and less siderophile with increasing carbon and nickel in the metal phase. We apply our new experiments with literature data for S partitioning to a core formation-primordial atmosphere degassing model. Our model demonstrates that the S/N ratio of the observable Earth can be set during primordial stratification under the same extreme P-T conditions that satisfy refractory siderophile element budgets while also yielding a bulk planet with S contents near that estimated from Earth's volatility trend.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.185
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations33
Published2021
Admission routes1
Has abstractyes

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