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Record W3007572705 · doi:10.1093/mnras/staa568

Chemical diversity of super-Earths as a consequence of formation

2020· article· en· W3007572705 on OpenAlexafffund
Jennifer Scora, Diana Valencia, Alessandro Morbidelli, Seth A. Jacobson

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space Administration
KeywordsPlanetPhysicsTerrestrial planetAstrobiologyPlanetary massSolar SystemPlanetary migrationAstronomyAstrophysicsPopulationPlanetary system

Abstract

fetched live from OpenAlex

ABSTRACT Recent observations of rocky super-Earths have revealed an apparent wider distribution of Fe/Mg ratios, or core to mantle ratios, than the planets in our Solar system. This study aims to understand how much of the chemical diversity in the super-Earth population can arise from giant impacts during planetary formation. Planet formation simulations have only recently begun to treat collisions more realistically in an attempt to replicate the planets in our Solar system. We investigate planet formation more generally by simulating the formation of rocky super-Earths with varying initial conditions using a version of symba, a gravitational N-body code, that incorporates realistic collisions. We track the maximum plausible change in composition after each impact. The final planets span a range of Fe/Mg ratios similar to the Solar system planets, but do not completely match the distribution in super-Earth data. We only form a few planets with minor iron-depletion, suggesting other mechanisms are at work. The most iron-rich planets have a lower Fe/Mg ratio than Mercury, and are less enriched than planets such as Kepler-100b. This indicates that further work on our understanding of planet formation and further improvement of precision of mass and radius measurements are required to explain planets at the extremes of this Fe/Mg distribution.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.296

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 head0.011
GPT teacher head0.185
Teacher spread0.174 · 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 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

Citations51
Published2020
Admission routes2
Has abstractyes

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