Assessing the dust selection bias in quasar absorbers at : Zn/Fe abundances in a radio-selected sample
Bibliographic record
Abstract
The Complete Optical and Radio Absorption Line System (CORALS) survey has previously been used to demonstrate that the number density, gas and metals content of z > 1.6 damped Lyman alpha (DLA) systems is not significantly underestimated in magnitude limited surveys. In this paper, a sample of strong Mg ii absorbers selected from the optically complete 0.7 < z < 1.6 CORALS sample of Ellison et al. is used to assess the potential of dust bias at intermediate redshifts. From echelle spectra of all CORALS absorbers with Mg iiλ2796 and Fe iiλ2600 rest equivalent widths >0.5 Å in the redshift range 0.7 < z < 1.6, we determine column densities of Zn, Cr, Fe, Mn and Si. The range of dust-to-metals ratios and inferred number density of DLAs from the D-index are consistent with optical samples. We also report the discovery of four new absorbers in the echelle data in the redshift range 1.7 < z < 2.0, two of which are confirmed DLAs and one is a sub-DLA, whilst the Lyα line is not covered for the fourth.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".