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1420 MHz Continuum Absorption Towards Extragalactic Sources in the Galactic Plane

2004· preprint· en· W6891832809 on OpenAlexaffabout

Bibliographic record

VenueCERN Document Server (European Organization for Nuclear Research) · 2004
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGalactic planeSpectral lineAbsorption (acoustics)Emission spectrumAbsorption spectroscopySpectroscopyHydrogen

Abstract

fetched live from OpenAlex

We present a 21-cm emission-absorption study towards extragalactic sources in the Canadian Galactic Plane Survey (CGPS). We have analyzed HI spectra towards 437 sources with S > 150 mJy, giving us a source density of 0.6 sources per square degree at arcminute resolution. We present the results of a first analysis of the HI temperatures, densities, and feature statistics. Particular emphasis is placed on 5 features with observed spin temperatures below 40 K. We find most spin temperatures in the range from 40 K to 300 K. A simple HI two-component model constrains the bulk of the cold component to temperatures (T_c) between 40 K and 100 K. T_c peaks in the Perseus arm region and clearly drops off with Galactocentric radius, R, beyond that. The HI density follows this trend, ranging from a local value of 0.4 cm^{-3} to less than 0.1 cm^{-3} at R = 20 kpc. We find that HI emission alone on average traces about 75% of the total HI column density, as compared to the total inferred by the emission and absorption. Comparing the neutral hydrogen absorption to CO emission no correlation is found in general, but all strong CO emission is accompanied by a visible HI spectral feature. Finally, the number of spectral HI absorption features per kpc drop off exponentially with increasing R.

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 score0.366
Threshold uncertainty score0.727

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
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 head0.026
GPT teacher head0.271
Teacher spread0.245 · 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

Citations0
Published2004
Admission routes2
Has abstractyes

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