Bibliographic record
Abstract
DECaPS Stellar Inference Constraints on the distances, extinctions, and stellar types for 709 million stars used in the construction of the DECaPS 3D dust map from Zucker, Saydjari, and Speagle et al. 2025. For those not needing the full stellar sample, this catalog is also available to query via NOIRLab’s AstroDataLab and accessible via TAP-accessible clients, including the astroquery Python package (see decaps dr2.stellar inference). Identifiers decaps_id: Unique DECaPS2 object ID from Saydjari et al. 2023 gaia_id: Gaia DR3 ID Astrometric Data parallax: Gaia DR3 parallax in mas parallax_error: Gaia DR3 parallax error in mas ra: Right Ascension from DECaPS2 in deg dec: Declination from DECaPS2 in deg Photometric Data decaps_fracflux (x5): Fraction of flux in this object's PSF that comes from this object in each DECaPS band mag (x13): Magnitudes from DECaPS2 (grizY), VVV (JHK), 2MASS (JHK), and unWISE (W1, W2) in mag magerr (x13): Magnitude errors from DECaPS2, VVV, 2MASS, and unWISE in mag Samples and χ² chi2: Best-fit χ² value from models used in the fit samps_dist (x5): Five random samples of distance in kpc samps_extinction (x5): Five random samples of AV in mag samps_rv (x5): Five random samples of RV samps_models (x5): Five random samples of the model index for accessing the input parameter grid (grid_mist_v10.h5) Percentile Data dist (x5): 2.5th, 16th, 50th, 84th, 97.5th percentiles of the samples distance in kpc rv (x5): 2.5th, 16th, 50th, 84th, 97.5th percentiles of the samples in RV eep (x5): 2.5th, 16th, 50th, 84th, 97.5th percentiles of the samples in MIST EEP feh (x5): 2.5th, 16th, 50th, 84th, 97.5th percentiles of the samples FeH loga (x5): 2.5th, 16th, 50th, 84th, 97.5th percentiles of the samples log age in years logg (x5): 2.5th, 16th, 50th, 84th, 97.5th percentiles of the samples in surface gravity logl (x5): 2.5th, 16th, 50th, 84th, 97.5th percentiles of the samples in log Lbol in L⊙ logt (x5): 2.5th, 16th, 50th, 84th, 97.5th percentiles of the samples in log Teff in K mini (x5): 2.5th, 16th, 50th, 84th, 97.5th percentiles of the samples in initial mass in M⊙ extinction (x5): 2.5th, 16th, 50th, 84th, 97.5th percentiles of the samples in AV in mag
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.280 | 0.327 |
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".