The Metal‐strong Damped Lyα Systems
Bibliographic record
Abstract
We have identified a metal-strong (logN(Zn+) > 13.15 or logN(Si+) > 15.95) DLA (MSDLA) population from an automated quasar (QSO) absorber search in the Sloan Digital Sky Survey Data Release 3 (SDSS-DR3) quasar sample, and find that MSDLAs comprise ~5% of the entire DLA population with z_abs > 2.2 found in QSO sightlines with r < 19.5. We have also acquired 27 Keck ESI follow-up spectra of metal-strong candidates to evaluate our automated technique and examine the MSDLA candidates at higher resolution. We demonstrate that the rest equivalent widths of strong ZnII 2026 and SiII 1808 lines in low-resolution SDSS spectra are accurate metal-strong indicators for higher-resolution spectra, and predict the observed equivalent widths 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(HI)-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 supernovae enrichment are differential depletion effects due to dust (and in a few cases 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 KeckI/HIRES and VLT/UVES spectra.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".