Bibliographic record
Abstract
We present a new method based on H I column densities for determination of distances within the disk of the Galaxy. The technique is useful for all Galactic plane objects, including H II regions and supernova remnants (SNRs), provided a line-of-sight velocity can be assigned to the object. Our method uses 21 cm spectral-line data to find the atomic hydrogen column density to an object, and beyond it to the Galactic edge. A model of the smooth large-scale Galactic distribution of H I material seen in emission (which principally traces the smooth structure of the Galaxy) is constructed. Our model accounts for scale-height flaring with increasing Galactocentric radius and includes the Galactic warp, which is prominent in the first and second quadrants of the Galaxy. The model's ability to trace the observed distribution of H I is demonstrated on lines of sight toward SNR DA 530 (l = 933, b = 7°) and H II region Sh 121 (l = 902, b = 17). We then apply the new technique to 29 Sharpless H II regions with known photometric distances across the second quadrant. We measure line-of-sight velocities for the H II regions from associated 12CO emission, using 1' resolution 12CO (J = 1-0) data from the Canadian Galactic Plane Survey. Our distance method yields distances to these objects that are consistent with their photometric distances and which are markedly smaller than the kinematic distances found from a flat Galactic rotation curve.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".