On the use of <i>Gaia</i> magnitudes and new tables of bolometric corrections
Bibliographic record
Abstract
Abstract The availability of reliable bolometric corrections and reddening estimates, rather than the quality of parallaxes, will be one of the main limiting factors in determining the luminosities of a large fraction of Gaia stars. With this goal in mind, we provide GaiaGBP, G, and GRP synthetic photometry for the entire MARCS grid and test the performance of our synthetic colours and bolometric corrections against space-borne absolute spectrophotometry. We find indication of a magnitude-dependent offset in Gaia DR2 G magnitudes, which must be taken into account in high-accuracy investigations. Our interpolation routines are easily used to derive bolometric corrections at desired stellar parameters and to explore the dependence of Gaia photometry on Teff, $\log \,g$, $\rm {[Fe/H]}$, $[\alpha /\rm {Fe}]$, and E(B − V). Gaia colours for the Sun and Vega, and Teff-dependent extinction coefficients are also provided.
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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 | 0.014 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".