The Line-of-Sight Depth of Populous Clusters in the Small Magellanic Cloud
Bibliographic record
Abstract
We present an analysis of age, metal abundance, and positional data on populous clusters in the Small Magellanic Cloud (SMC) with the ultimate aim of determining the line-of-sight (LOS) depth of the SMC by using these clusters as proxies. Our data set contains 12 objects and is limited to clusters with the highest-quality data for which the ages and abundances are best known and can be placed on an internally consistent scale. We have analyzed the variation of the clusters' properties with position on the sky and with line-of-sight depth. Based on this analysis, we draw the following conclusions: (1) The observational data indicate that the eastern side of the SMC (facing the Large Magellanic Cloud) contains younger and more metal-rich clusters as compared with the western side. This is not a strong correlation because our data set of clusters is necessarily limited, but it is suggestive and warrants further study. (2) Depending on how the reddening is computed to our clusters, we find a mean distance modulus that ranges from ( m - M ) 0 = 18.71 ± 0.06 to 18.82 ± 0.05. (3) The intrinsic ±1 σ LOS depth of the SMC populous clusters in our study is between ∼6 and ∼12 kpc, depending primarily on whether we adopt the Burstein & Heiles reddenings or those from Schlegel et al. (4) Viewing the SMC as a triaxial galaxy with declination, right ascension, and LOS depth as the three axes, we find axial ratios of approximately 1 : 2 : 4. Taken together, these conclusions largely agree with those of previous investigators and underscore the utility of populous star clusters as probes of the structure of the Small Magellanic Cloud.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".