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Record W2152655152 · doi:10.1017/s174392131400163x

The Role of Magnetic Fields in Star Formation

2013· article· en· W2152655152 on OpenAlexaff
Ralph E. Pudritz, Mikhail Klassen, Helen Kirk, Daniel Seifried, Robi Banerjee

Bibliographic record

VenueProceedings of the International Astronomical Union · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhysicsMolecular cloudStar formationAstrophysicsStarsAngular momentumMagnetic fieldMagnetohydrodynamicsGravitational collapseAstronomyFragmentation (computing)Classical mechanics

Abstract

fetched live from OpenAlex

Abstract Stars are born in turbulent, magnetized filamentary molecular clouds, typically as members of star clusters. Several remarkable technical advances enable observations of magnetic structure and field strengths across many physical scales, from galactic scales on which giant molecular clouds (GMCs) are assembled, down to the surfaces of magnetized accreting young stars. These are shedding new light on the role of magnetic fields in star formation. Magnetic fields affect the gravitational fragmentation and formation of filamentary molecular clouds, the formation and fragmentation of magnetized disks, and finally to the shedding of excess angular momentum in jets and outflows from both the disks and young stars. Magnetic fields play a particularly important role in angular momentum transport on all of these scales. Numerical simulations have provided an important tool for tracking the complex process of the collapse and evolution of protostellar gas since several competing physical processes are at play - turbulence, gravity, MHD, and radiation fields. This paper focuses on the role of magnetic fields in three crucial regimes of star formation: the formation of star clusters emphasizing fragmentation, disk formation and the origin of early jets and outflows, to processes that control the spin evolution of young stars.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.146

Codex and Gemma teacher scores by category

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 head0.004
GPT teacher head0.189
Teacher spread0.185 · 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 teacher head, 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

Citations8
Published2013
Admission routes1
Has abstractyes

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