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Record W2099533562 · doi:10.1002/asna.200510450

Accretion in brown dwarfs down to nearly planetary masses

2005· article· en· W2099533562 on OpenAlexaff
Subhanjoy Mohanty, Gibor Basri, Ray Jayawardhana

Bibliographic record

VenueAstronomische Nachrichten · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhysicsAstrophysicsT Tauri starBrown dwarfAccretion (finance)Low MassStarsEmission spectrumFlux (metallurgy)Effective temperatureSpectral linePlanetary massAstronomyMass fluxPlanetary system

Abstract

fetched live from OpenAlex

Abstract We show that in accreting ultra low‐mass stars and brown dwarfs, the CaII λ 8662 emission line flux correlates remarkably well with the mass accretion rate ($ \dot M $), just as it does in higher mass classical T Tauri stars (CTTs). A straightforward measurement of the CaII flux thus provides an easier $ \dot M $ determination technique than detailed modeling of the Hα emission line profile (except at the very lowest accretion rates, where CaII does not appear to be in emission for ultra low‐mass objects, and Hα modeling is required). Using optical high‐resolution spectra, we infer $ \dot M $ from CaII emission for young ultra low‐mass objects down to nearly the deuterium‐burning (planetary‐mass) limit. Our results, in combination with previous determinations of $ \dot M $ in CTTs, illustrate that the accretion rate declines steeply with mass, roughly as $ \dot M $ ∝ M*2 (albeit with considerable scatter). A similar relationship has been suggested by previous studies; we extend it down to nearly the planetary regime. The physical reason for this phenomenon is not yet clear; we discuss various possible mechanisms. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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)0.0000.000

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 head0.009
GPT teacher head0.231
Teacher spread0.223 · 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 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

Citations18
Published2005
Admission routes1
Has abstractyes

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