Milky Way demographics with the VVV survey
Bibliographic record
Abstract
The new multi-epoch near-infrared VISTA Variables in the Vía Láctea (VVV) survey is sampling 562 deg2 of the Galactic bulge and adjacent regions of the disk. Accurate astrometry established for the region surveyed allows the VVV data to be merged with overlapping surveys (e.g., GLIMPSE, WISE, 2MASS, etc.), thereby enabling the construction of longer baseline spectral energy distributions for astronomical targets. However, in order to maximize use of the VVV data, a set of transformation equations are required to place the VVV JHKs photometry onto the 2MASS system. The impetus for this work is to develop those transformations via a comparison of 2MASS targets in 152 VVV fields sampling the Galactic disk. The transformation coefficients derived exhibit a reliance on variables such as extinction. The transformed data were subsequently employed to establish a mean reddening law of EJ−H/EH−Ks = 2.13 ± 0.04, which is the most precise determination to date and merely emphasizes the pertinence of the VVV data for determining such important parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".