The Belgian repository of fundamental atomic data and stellar spectra
Bibliographic record
Abstract
BRASS is an international networking project of the Federal Government of Belgium for the development of a new public database providing accurate fundamental atomic data of vital importance for stellar spectroscopic research. The BRASS database will offer atomic line data that is thoroughly tested by comparing theoretical and observed stellar spectra. We are in the course of performing extensive quality assessments of selected atomic input data with advanced radiative transfer spectrum synthesis calculations that we compare in detail to high-resolution Mercator-HERMES and ESO-VLT-UVES spectra of very high signal-to-noise ratios for about 30 hot and cool bright stars of B, A, F, G, and K spectral types. The new database will provide the tested and validated values of absorption lines we retrieve from various existing atomic repositories, such as NIST and VAMDC. The validated atomic datasets, combined with the observed and theoretical spectra, will be interactively offered online at brass.sdf.org. The combination of these datasets is a novel approach for its development, which will provide a universal reference for advanced stellar spectroscopic research. We present the atmospheric parameter results of a subset of five benchmark stars observed with signal-to-noise ratios of 800–1200. The observed and theoretical spectra of the Sun and 51 Peg between 4000 and 6800 Å are offered online in the BRASS Data Interface. It also incorporates a new list of ∼900 metal lines for which we compute blending below 5% of the equivalent width useful for detailed line profile modeling and synthetic spectrum fit quality assessments of atomic line data.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.046 |
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".