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Record W2492161644 · doi:10.3847/2041-8205/827/2/l27

A HIGH-VELOCITY CLOUD IMPACT FORMING A SUPERSHELL IN THE MILKY WAY

2016· article· en· W2492161644 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal Letters · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsOkanagan College
Fundersnot available
KeywordsMilky WayIntergalactic travelGalaxyGalactic haloSupernovaHaloGalactic tideGalactic Center

Abstract

fetched live from OpenAlex

ABSTRACT Neutral atomic hydrogen (H i) gas in interstellar space is largely organized into filaments, loops, and shells, the most prominent of which are “supershells.” These gigantic structures, which require erg to form, are generally thought to be produced by either the explosion of multiple supernovae (SNe) in OB associations or, alternatively, by the impact of high-velocity clouds (HVCs) falling into the Galactic disk. Here, we report the detection of a kiloparsec (kpc)-size supershell in the outskirts of the Milky Way with the compact HVC 040 + 01−282 (hereafter, CHVC040) at its geometrical center using the “Inner-Galaxy Arecibo L-band Feed Array” H i 21 cm survey data. The morphological and physical properties of both objects suggest that CHVC040, which is either a fragment of a nearby disrupted galaxy or a cloud that originated from an intergalactic accreting flow, collided with the disk ∼5 Myr ago to form the supershell. Our results show that some compact HVCs can survive their trip through the Galactic halo and inject energy and momentum into the Milky Way disk.

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)0.000
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.233
Teacher spread0.223 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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