Bibliographic record
Abstract
We analyze the opportunities in and limits to investigating quasars with the Gaia satellite by studying Gaia’s low- and high-resolution quasar spectra, with consideration of their signal-to-noise ratios. Furthermore, we explore bright quasars from the Sloan Digital Sky Survey with broad emission lines (BELs) redshifted into the spectral range of Gaia’s Radial Velocity Spectrograph (RVS). We find that Gaia low-resolution spectra of quasars enable a determination of equivalent widths, continuum variability, and the Baldwin effect. Additionally, it will be feasible to analyze BEL reverberation mapping with Gaia data for a small sample of objects. These quasars should have a high cadence of measurements or higher time lags due to large redshifts, high quasar luminosities, or selected low-ionization lines. More than 500 known quasars will also get high-resolution spectra of individual BELs in the small wavelength range of the RVS. This allows an investigation of broad emission line shapes and their variabilities to get information on the spatial structure and kinematics of the broad line region. We identify six known variable SDSS quasars with BELs in the RVS that have interesting spectra for a potential intrinsic line variability investigation. However, the signal-to-noise ratio of the RVS is too small for studying narrow and broad absorption lines in quasar spectra.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".