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Record W3101989524

GSH 138-01-94, an old supernova remnant in the far outer Galaxy

2001· article· en· W3101989524 on OpenAlexaffabout

Bibliographic record

VenueCERN Document Server (European Organization for Nuclear Research) · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsQueen's University
Fundersnot available
KeywordsPhysicsAstrophysicsGalaxySupernovaSupernova remnantInterstellar mediumMetallicityAstronomyGalactic planeRADIUS
DOInot available

Abstract

fetched live from OpenAlex

The properties of the Galactic HI shell GSH 138-01-94 are derived from data of the Canadian Galactic Plane Survey. The basic parameters of GSH 138-01-94 were determined by fitting the expansion of a thin shell to the expansion velocity field on the sky. The kinematic distance is 16.6 kpc for v_LSR=-94.2 $\\pm$ 0.5 km/s. The radius is 180 $\\pm$ 10 pc, the expansion velocity v_exp = 11.8 $\\pm$ 0.9 km/s, and the mass 2 * 10^5 M_sun. No radio continuum counterpart of the shell was detected at 21 cm or at 74 cm. Absorption of a background continuum source constrains the spin temperature of \\HI in the shell to T_s=230^{367}_{173} K. The expansion age of GSH 138-01-94 is 4.3 Myr. These observables are in excellent agreement with predictions from hydrodynamic models for a supernova remnant in a low-density low-metallicity environment such as the outer Galaxy. GSH 138-01-94 is then the largest and the oldest supernova remnant known. It provides direct evidence for the release of mechanical energy in the interstellar medium by stars in the outer galaxy. It is argued that such old supernova remnants be found in low-density, low-metallicity environments such as the outer Galaxy, dwarf galaxies and low surface brightness galaxies.

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 score0.152
Threshold uncertainty score0.302

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.024
GPT teacher head0.258
Teacher spread0.234 · 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

Citations20
Published2001
Admission routes2
Has abstractyes

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