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Record W3036999981 · doi:10.3847/1538-4357/ab9f9d

The Effect of Misalignment between the Rotation Axis and Magnetic Field on the Circumstellar Disk

2020· article· en· W3036999981 on OpenAlexaff
Shingo Hirano, Yusuke Tsukamoto, Shantanu Basu, Masahiro N. Machida

Bibliographic record

VenueThe Astrophysical Journal · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsOutflowPhysicsProtostarRotation (mathematics)AstrophysicsMagnetic fieldMagnetohydrodynamic driveCircumstellar diskMagnetohydrodynamicsBipolar outflowThermalStar formationStarsGeometryMeteorology

Abstract

fetched live from OpenAlex

Abstract The formation of circumstellar disks is investigated using three-dimensional resistive magnetohydrodynamic simulations in which the initial prestellar cloud has a misaligned rotation axis with respect to the magnetic field. We examine the effects of (i) the initial angle difference between the global magnetic field and the cloud rotation axis ( θ 0 ) and (ii) the ratio of the thermal to gravitational energy ( α 0 ). We study 16 models in total and calculate the cloud evolution until ∼5000 yr after protostar formation. Our simulation results indicate that an initial nonzero θ 0 (>0) promotes disk formation but tends to suppress outflow driving for models that are moderately gravitationally unstable, α 0 ≲ 1. In these models, a large-sized rotationally supported disk forms and a weak outflow appears, in contrast to a smaller disk and strong outflow in the aligned case ( θ 0 = 0). Furthermore, we find that when the initial cloud is highly unstable with small α 0 , the initial angle difference θ 0 does not significantly affect the disk formation and outflow driving.

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 score0.008
Threshold uncertainty score0.016

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 studies0.0000.001
Scholarly communication0.0010.000
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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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

Citations50
Published2020
Admission routes1
Has abstractyes

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