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Record W2134024446 · doi:10.1088/2041-8205/710/2/l167

DEAD ZONES AS THERMAL BARRIERS TO RAPID PLANETARY MIGRATION IN PROTOPLANETARY DISKS

2010· article· en· W2134024446 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal Letters · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsThermalRadiative transferProtoplanetary diskSettlingDead zonePlanetPlanetesimal

Abstract

fetched live from OpenAlex

Planetary migration in standard models of gaseous protoplanetary disks is known to be very rapid (∼10 5 years), jeopardizing the existence of planetary systems. We present a new mechanism for significantly slowing rapid planetary migration, discovered by means of radiative transfer calculations of the thermal structure of protoplanetary disks irradiated by their central stars. Rapid dust settling in a disk's dead zone—a region with very little turbulence—leaves a dusty wall at its outer edge. We show that the back-heating of the dead zone by this irradiated wall produces a positive gradient of the disk temperature, which acts as a thermal barrier to planetary migration which persists for the disk lifetime. Although we analyze in detail the migration of a super-Earth in a low-mass disk around an M star, our findings can apply to a wide variety of young planetary systems. We compare our findings with other potentially important stopping mechanisms and show that there are large parameter spaces for which dead zones are likely to play the most important role for reproducing the observed mass–period relation in longer planetary periods.

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)0.002
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.006
GPT teacher head0.214
Teacher spread0.209 · 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

Citations36
Published2010
Admission routes1
Has abstractyes

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