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Record W1648277683 · doi:10.1088/0004-637x/809/2/153

GHIGLS: H I MAPPING AT INTERMEDIATE GALACTIC LATITUDE USING THE GREEN BANK TELESCOPE

2015· article· en· W1648277683 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of TorontoUniversité LavalCanadian Institute for Theoretical Astrophysics
Fundersnot available
KeywordsSpectral lineInterstellar mediumGreen Bank TelescopeLine (geometry)EclipticExponentLatitudeScaling

Abstract

fetched live from OpenAlex

This paper introduces the data cubes from GHIGLS, deep Green Bank Telescope surveys of the 21-cm line emission of HI in 37 targeted fields at intermediate Galactic latitude. The GHIGLS fields together cover over 1000 square degrees at 9.55' spatial resolution. The HI spectra have an effective velocity resolution about 1.0 km/s and cover at least -450 < v < +250 km/s. GHIGLS highlights that even at intermediate Galactic latitude the interstellar medium is very complex. Spatial structure of the HI is quantified through power spectra of maps of the column density, NHI. For our featured representative field, centered on the North Ecliptic Pole, the scaling exponents in power-law representations of the power spectra of NHI maps for low, intermediate, and high velocity gas components (LVC, IVC, and HVC) are -2.86 +/- 0.04, -2.69 +/- 0.04, and -2.59 +/- 0.07, respectively. After Gaussian decomposition of the line profiles, NHI maps were also made corresponding to the narrow-line and broad-line components in the LVC range; for the narrow-line map the exponent is -1.9 +/- 0.1, reflecting more small scale structure in the cold neutral medium (CNM). There is evidence that filamentary structure in the HI CNM is oriented parallel to the Galactic magnetic field. The power spectrum analysis also offers insight into the various contributions to uncertainty in the data. The effect of 21-cm line opacity on the GHIGLS NHI maps is estimated.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.662

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.268
Teacher spread0.232 · 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 designOther design
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

Citations87
Published2015
Admission routes1
Has abstractyes

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