The Metal‐strong Damped Lyα Systems
Bibliographic record
Abstract
We have identified a metal‐strong [log N(Zn + )≥13.15 or log N(Si + )≥15.95] damped Lyα (MSDLA) population from an automated quasar (QSO) absorber search in the Sloan Digital Sky Survey (SDSS) Data Release 3 quasar sample and find that MSDLAs comprise ≈5% of the entire DLA population with z abs ≥2.2 found in QSO sight lines with r<19.5. We have also acquired 27 Keck ESI (Echellete Spectrograph and Imager) follow‐up spectra of metal‐strong candidates in order to evaluate our automated technique and examine the MSDLA candidates at higher resolution. We demonstrate that the rest equivalent widths of strong Zn ii λ2026 and Si ii λ1808 lines in low‐resolution SDSS spectra are accurate indicators of metal‐strong systems in higher resolution spectra, and predict the observed equivalent widths W obs and signal‐to‐noise ratios needed to detect certain extremely weak lines with high‐resolution instruments. We investigate how the MSDLAs may affect previous studies concerning a dust obscuration bias and the N (H i )–weighted cosmic mean metallicity 〈Z(z)〉. Finally, we include a brief discussion of abundance ratios in our ESI sample and find that underlying mostly Type II supernova enrichment are differential depletion effects due to dust (and in a few cases, these are quite strong); we present here a handful of new Ti and Mn measurements, both of which are useful probes of depletion in DLAs. Future papers will present detailed examinations of particularly metal‐strong DLAs from high‐resolution Keck I HIRES and VLT UVES 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 | 0.000 |
| 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) | 0.003 | 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".