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Record W2059952881 · doi:10.1088/0004-637x/721/2/1593

ON THE STABILITY OF DUST-LADEN PROTOPLANETARY VORTICES

2010· article· en· W2059952881 on OpenAlexaff
Philip Chang, Jeffrey S. Oishi

Bibliographic record

VenueThe Astrophysical Journal · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsCanadian Institute for Theoretical Astrophysics
Fundersnot available
KeywordsPlanetesimalPhysicsInstabilityVortexProtoplanetary diskAstrophysicsPlanetMechanics

Abstract

fetched live from OpenAlex

The formation of planetesimals via gravitational instability of the dust layer in a protoplanetary disks demands that there be local patches where dust is concentrated by a factor of a few × 10 3 over the background value. Vortices in protoplanetary disks may concentrate dust to these values, allowing them to be the nurseries of planetesimals. The concentration of dust in the cores of vortices increases the dust–gas ratio of the core compared to the background disk, creating a "heavy vortex." In this work, we show that these vortices are subject to an instability which we have called the heavy-core instability. Using Floquet theory, we show that this instability occurs in elliptical protoplanetary vortices when the gas–dust density of the core of the vortex is heavier than the ambient gas–dust density by a few tens of percent. The heavy-core instability grows very rapidly, with a growth timescale of a few vortex rotation periods. While the nonlinear evolution of this instability remains unknown, it will likely increase the velocity dispersion of the dust layer in the vortex because instability sets in well before sufficient dust can gather to form a protoplanetary seed. This instability may thus preclude vortices from being sites of planetesimal formation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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

Citations39
Published2010
Admission routes1
Has abstractyes

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