DECaPS 3D Dust Map and Stellar Inference Software
Bibliographic record
Abstract
Code needed to reproduce the results for the DECaPS stellar inference and 3D dust map from Zucker, Saydjari, & Speagle et al. 2025. Each zip folder contains the code for a different part of the pipeline: perstar.zip: Code needed to generate the stellar parameters (distance, extinction, and stellar type) for 709 million stars used in the creation of the 3D dust map. This includes exact brutus configuration that was run (see https://doi.org/10.5281/zenodo.14915000), as well as the submission of batch jobs on the Harvard Cannon cluster. LOS.zip: Code needed to infer the distribution of dust along each line of sight. This includes the scripts for repixelation (grouping the stars into NSIDE=8192 pixels), the Bayestar LOS compilation used for MCMC sampling, and the submission of batch jobs on the Harvard Cannon cluster infill.zip: Code needed to infill the map in regions where there were too few stars needed to run the line-of-sight fit, including the submission of batch jobs on the Harvard Cannon cluster.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.137 | 0.098 |
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".