A New View of Cold H<scp>i</scp>Clouds in the Milky Way
Bibliographic record
Abstract
We reveal cold Galactic clouds of neutral hydrogen in unprecedented detail. Our 21 cm synthesis maps, taken from the Canadian Galactic Plane Survey, show a numerous and diverse population of H I self-absorption (HISA) features in gas outside the solar circle. These objects vary in size, shape, and contrast against the background H I. All display a high level of angular and velocity structure, and most would appear significantly diluted, if not invisible, in lower resolution H I surveys. A number of Perseus arm features remain unresolved by the 1' beam of our survey, with apparent diameters less than 0.6 pc at 2 kpc distance. The majority of HISA features we detect have no obvious 12 CO emission counterparts. This suggests that either HISA is not found predominantly in molecular clouds, as has often been presumed in the past, or that CO is not a good tracer of H 2 . Some HISA lacking CO shows far-infrared dust emission, though whether this arises from shielded molecular gas or from diffuse atomic clouds is not clear. Constraining the gas properties of HISA remains a difficult problem, but we introduce a new method that aids this process. Our approach relates a number of physical parameters via gas law and line integral relationships and should prove powerful if the input variables are sufficiently well known. We explore the current allowed parameter ranges for three sample features of very different appearance. We find spin temperatures ≲50 K and densities ≳10 2 cm -3 .
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".