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Impact of internal gravity waves on the rotation profile inside pre-main sequence low-mass stars

2013· article· en· W3104433299 on OpenAlexaff
C. Charbonnel, T. Decressin, Louis Amard, A. Palacios, S. Talon

Bibliographic record

VenueSpringer Link (Chiba Institute of Technology) · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de Montréal
FundersCentre National de la Recherche ScientifiqueSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungAgence Nationale de la RechercheNational Science Foundation
KeywordsPhysicsInternal waveStarsMain sequenceInternal rotationAstrophysicsMetallicitySurface gravityAstronomyStellar rotationTurbulenceMechanics

Abstract

fetched live from OpenAlex

Aims. We study the impact of internal gravity waves (IGW), meridional circulation, shear turbulence, and stellar contraction on the internal rotation profile and surface velocity evolution of solar metallicity low-mass pre-main sequence stars. Methods. We compute a grid of rotating stellar evolution models with masses between 0.6 and 2.0 M⊙ taking these processes into account for the transport of angular momentum, as soon as the radiative core appears and assuming no more disk-locking from that moment on. IGW generation along the PMS is computed taking Reynolds-stress and buoyancy into account in the bulk of the stellar convective envelope and convective core (when present). Redistribution of angular momentum within the radiative layers accounts for damping of prograde and retrograde IGW by thermal diffusivity and viscosity in corotation resonance. Results. Over the whole mass range considered, IGW are found to be efficiently generated by the convective envelope and to slow down the stellar core early on the PMS. In stars more massive than ~1.6 M⊙, IGW produced by the convective core also contribute to angular momentum redistribution close to the ZAMS. Conclusions. Overall, IGW are found to significantly change the internal rotation profile of PMS low-mass stars.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.622

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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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

Citations31
Published2013
Admission routes1
Has abstractyes

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