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Record W2123808593 · doi:10.1088/0004-637x/768/2/159

MISALIGNMENT OF MAGNETIC FIELDS AND OUTFLOWS IN PROTOSTELLAR CORES

2013· article· en· W2123808593 on OpenAlexaff
Charles L. H. Hull, R. L. Plambeck, Alberto D. Bolatto, Geoffrey C. Bower, John M. Carpenter, R. M. Crutcher, Jason Fiege, Erica Franzmann, Nicholas S. Hakobian, Carl Heiles, Martin Houde, A. Meredith Hughes, Katherine Jameson, Woojin Kwon, James W. Lamb, Leslie W. Looney, Brenda C. Matthews, Lee G. Mundy, T. Pillai, Marc W. Pound, Ian Stephens, John Tobin, John E. Vaillancourt, N. H. Volgenau, M. C. H. Wright

Bibliographic record

VenueThe Astrophysical Journal · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of VictoriaNational Research Council CanadaWestern UniversityUniversity of Manitoba
Fundersnot available
KeywordsProtostarPhysicsAstrophysicsMagnetic fieldCircumstellar diskPolarization (electrochemistry)Star formationPerpendicularLow MassStarsGeometry

Abstract

fetched live from OpenAlex

We present results of λ1.3 mm dust-polarization observations toward 16 nearby, low-mass protostars, mapped with ∼2 5 resolution at CARMA. The results show that magnetic fields in protostellar cores on scales of ∼1000 AU are not tightly aligned with outflows from the protostars. Rather, the data are consistent with scenarios where outflows and magnetic fields are preferentially misaligned (perpendicular), or where they are randomly aligned. If one assumes that outflows emerge along the rotation axes of circumstellar disks, and that the outflows have not disrupted the fields in the surrounding material, then our results imply that the disks are not aligned with the fields in the cores from which they formed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

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 studies0.0000.000
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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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 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

Citations168
Published2013
Admission routes1
Has abstractyes

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