GALEX absolute calibration and extinction coefficients based on white dwarfs
Bibliographic record
Abstract
ABSTRACT We use 1837 DA white dwarfs with high signal-to-noise ratio spectra and Gaia parallaxes to verify the absolute calibration and extinction coefficients for the Galaxy Evolution Explorer (GALEX). We use white dwarfs within 100 pc to verify the linearity correction to the GALEX data. We find that the linearity correction is valid for magnitudes brighter than 15.95 and 16.95 for the far-ultraviolet (FUV) and near-ultraviolet (NUV) bands, respectively. We also use DA white dwarfs beyond 250 pc to calculate extinction coefficients in the FUV and NUV bands: RFUV = 8.01 ± 0.07 and RNUV = 6.72 ± 0.04. These are consistent with the predicted extinction coefficients for Milky Way-type dust in the FUV, but smaller than predictions in the NUV. With well understood optical spectra and state-of-the-art model atmosphere analysis, these white dwarfs currently provide the best constraints on the extinction coefficients for the GALEX 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".