Bibliographic record
Abstract
DECaPS 3D Dust Map A three-dimensional map of dust reddening, covering the Southern Galactic plane (239° < l < 6°, |b| < 10°). We use DECaPS optical and near-infrared photometry, in conjunction with complementary photometric and astrometric data, to infer distances and reddenings to 709 million stars. These stars trace the reddening along different lines of sight, allowing us to build up a map of reddening in 3D. The map is structured as a set of sightlines, each of which contains multiple samples of the cumulative dust reddening as a function of distance. Each sightline is identified by a nested pixel index at a HEALPix Nside = 8192. Within each sightline, cumulative reddening is given at discrete distances, spaced evenly in distance modulus. Quality Assurance Information Quality assurance information is given for each pixel, including: Whether the fit converged in the pixel Whether the pixel needed to be infilled due to lack of stars The minimum reliable distance modulus in the pixel The maximum reliable distance moduli in the pixel Alongside the quality assurance information, we also provide the number of stars whose PDF on distance and reddening is used to inform the line of sight fit towards each pixel. Note that this will not sum to the total number of stars used in the construction of the map (709 million) because some stars contribute to multiple pixels based on our Gaussian weighting scheme. Unlike the "Bayestar" 3D dust map from Green et al. 2019 (whose reddening is provided in an arbitrary reddening unit), the DECaPS 3D dust reddening is given in units of E(B-V) in mags. To convert to A(V), assume R(V)=3.32. When combined with Bayestar, the DECaPS map enables extinction corrections over the entire Galactic plane |b| < 10°. The 3D map is described in more detail in Zucker, Saydjari, and Speagle et al. 2025. Downloading and Querying the Map This map is included in the Python dustmaps package, which is available through pip: pip install dustmaps Note that we generate 100 samples of the reddening. In the data product decaps_mean_and_samples.h5, we calculate the mean from the 100 samples and additionally provide five random samples, all stored as float16 to reduce memory usage (33 GB in size). For those with limited disk space, in decaps_mean.h5, we provide the option to only download the mean map (no samples), which is significantly smaller in size (8 GB). Note that in August 2025, we updated these files to increase the precision of our minimum and maximum reliable distance moduli (from float16 to float32). This change affected less than 1% of pixels (by a very small fractional amount) and should not affect any queries performed prior to August 2025.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.126 | 0.114 |
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".