CHANG-ES XXV: H <scp>i</scp>imaging of nearby edge-on galaxies – Data Release 4
Bibliographic record
Abstract
ABSTRACT We present the ${\rm H}\, {\small I}$ distribution of galaxies from the Continuum Haloes in Nearby Galaxies – an EVLA Survey (CHANG-ES). Though the observational mode was not optimized for detecting ${\rm H}\, {\small I}$, we successfully produce ${\rm H}\, {\small I}$ cubes for 19 galaxies. The moment-0 maps from this work are available on CHANG-ES data release website (i.e. https://www.queensu.ca/changes). Our sample is dominated by star-forming, ${\rm H}\, {\small I}$-rich galaxies at distances from 6.27 to 34.1 Mpc. ${\rm H}\, {\small I}$ interferometric images on two of these galaxies (NGC 5792 and UGC 10288) are presented here for the first time, while 12 of our remaining sample galaxies now have better ${\rm H}\, {\small I}$ spatial resolutions and/or sensitivities of intensity maps than those in existing publications. We characterize the average scale heights of the ${\rm H}\, {\small I}$ distributions for a subset of most inclined galaxies (inclination > 80 deg), and compare them to the radio continuum intensity scale heights, which have been derived in a similar way. The two types of scale heights are well correlated, with similar dependence on disc radial extension and star formation rate surface density but different dependence on mass surface density. This result indicates that the vertical distribution of the two components may be governed by similar fundamental physics but with subtle differences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 0.029 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".