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Record W4402140882 · doi:10.3847/1538-4357/ad6444

Water Evolution and Inventories of Super-Earths Orbiting Late M Dwarfs

2024· article· en· W4402140882 on OpenAlexafffund
Keavin Moore, Benjamin David, Albert Yian Zhang, Nicolas B. Cowan

Bibliographic record

VenueThe Astrophysical Journal · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPhysicsAstronomyBrown dwarfAstrophysicsExoplanetWhite dwarfStarsMassive compact halo objectStellar evolutionAstrobiology

Abstract

fetched live from OpenAlex

Abstract Super-Earths orbiting M dwarf stars may be the most common habitable planets in the Universe. However, their habitability is threatened by intense irradiation from their host stars, which drives the escape of water to space and can lead to surface desiccation. We present simulation results of a box model incorporating deep-water cycling between interior and atmosphere and water loss to space for terrestrial planets of mass 1–8 M ⊕ orbiting in the habitable zone of a late M dwarf. Energy-limited loss decreases with planetary mass, while diffusion-limited loss increases with mass. Depending on where it orbits in the habitable zone, a 1 M ⊕ planet that starts with 3–8 Earth Oceans can end up with an Earthlike surface of oceans and exposed continents; for an 8 M ⊕ super-Earth, that range is 3–12 Earth Oceans. Planets initialized with more water end up as waterworlds with no exposed continents, while planets that start with less water have desiccated surfaces by 5 Gyr. Since the mantles of terrestrial planets can hold much more water than is currently present in Earth’s atmosphere, none of our simulations result in Dune planets—such planets may be less common than previously thought. Further, more water becomes sequestered within the mantle for larger planets. A super-Earth at the inner edge of the habitable zone tends to end up as either a waterworld or with a desiccated surface; only a narrow range of initial water inventory yields an Earthlike surface.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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