MétaCan
Menu
Back to cohort
Record W3163313444

The effect of core formation on surface composition and planetary habitability

2022· article· en· W3163313444 on OpenAlexaff

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsHabitabilityAstrobiologyComposition (language)Core (optical fiber)Surface (topology)Environmental sciencePhysicsPlanetAstronomyMathematicsGeometryOptics
DOInot available

Abstract

fetched live from OpenAlex

The melt productivity of a differentiated planet's mantle is primarily controlled by its iron content, which is itself approximated by the planet's core mass fraction (CMF). Here we show that estimates of an exoplanet's CMF allows robust predictions of the thickness, composition, and mineralogy of the derivative crust. These predicted crustal compositions allow constraints to be placed on volatile cycling between surface and the deep planetary interior, with implications for the evolution of habitable planetary surfaces. Planets with large, terrestrial-like CMFs (≥0.32) will exhibit thin crusts that are inefficient at transporting surface water and other volatiles into the underlying mantle. By contrast, rocky planets with smaller CMFs (≤0.24) and higher, Mars-like, mantle iron contents will develop thick crusts capable of stabilizing hydrous minerals, which can effectively sequester volatiles into planetary interiors and act to remove surface water over timescales relevant to evolution. The extent of core formation has profound consequences for the subsequent planetary surface environment and may provide additional constraints in the hunt for habitable, Earth-like exoplanets.

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 categoriesScience and technology studies, Insufficient 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 score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.213
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

Citations13
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueOxford University Research Archive (ORA) (University of Oxford)Same topicGeological and Geochemical AnalysisFrench-language works237,207