Chemical abundances of the damped Lyman α systems in the XQ-100 survey
Bibliographic record
Abstract
The XQ-100 survey has provided high signal-noise spectra of 100 redshift 3–4.5 quasars with the X-Shooter spectrograph. The metal abundances for 13 elements in the 41 damped Ly α systems (DLAs) identified in the XQ-100 sample are presented, and an investigation into abundances of a variety of DLA classes is conducted. The XQ-100 DLA sample contains five DLAs within 5000 km s−1 of their host quasar (proximate DLAs; PDLAs) as well as three sightlines which contain two DLAs within 10 000 km s−1 of each other along the same line of sight (multiple DLAs; MDLAs). Combined with previous observations in the literature, we demonstrate that PDLAs with log N(H i) < 21.0 show lower [S/H] and [Fe/H] [relative to intervening systems with similar redshift and N(H i)], whilst higher [S/H] and [Si/H] are seen in PDLAs with log N(H i) > 21.0. These abundance discrepancies are independent of their line-of-sight velocity separation from the host quasar, and the velocity width of the metal lines (v90). Contrary to previous studies, MDLAs show no difference in [α/Fe] relative to single DLAs matched in metallicity and redshift. In addition, we present follow-up UVES data of J0034+1639, a sightline containing three DLAs, including a metal-poor DLA with [Fe/H] = −2.82 (the third lowest [Fe/H] in DLAs identified to date) at zabs = 4.25. Lastly we study the dust-corrected [Zn/Fe], emphasizing that near-IR coverage of X-Shooter provides unprecedented access to Mg ii, Ca ii and Ti ii lines (at redshifts 3–4) to provide additional evidence for subsolar [Zn/Fe] ratio in DLAs.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".