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Record W4221091829 · doi:10.3847/1538-4357/ac53ac

Dust Dynamics in Transitional Disks: Clumping and Disk Recession

2022· article· en· W4221091829 on OpenAlexaff
Jiaqing Bi, Jeffrey Fung

Bibliographic record

VenueThe Astrophysical Journal · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsRotational symmetryMechanicsDragRadiationPhysicsDynamics (music)InstabilityEnhanced Data Rates for GSM EvolutionRadiation pressureFlow (mathematics)AstrophysicsOptics

Abstract

fetched live from OpenAlex

Abstract The role of radiation pressure in dust migration and the opening of inner cavities in transitional disks is revisited in this paper. Dust dynamics including radiation pressure is often studied in axisymmetric models, but in this work, we show that highly non-axisymmetric features can arise from an instability at the inner disk edge. Dust grains clump into high density features there, allowing radiation to leak around them and penetrate deeper into the disk, changing the course of dust migration. Our proof-of-concept, two-dimensional, vertically averaged simulations show that the combination of radiation pressure, shadowing, and gas drag can produce a net outward migration, or recession, of the dust component of the disk. The recession speed of the inner disk edge is on the order of 10−5 times Keplerian speed in our parameter space, which is faster than the background viscous flow, assuming a Shakura–Sunyaev viscosity α ≲ 10−3. This speed, if sustained over the lifetime of the disk, can result in a dust cavity as large as tens of astronomical units.

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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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