A search for damped Lyman systems towards radio-loud quasars I: the optical survey
Bibliographic record
Abstract
We present the results from the optical component of a survey for damped Lyman α systems (DLAs) towards radio-loud quasars. Our quasar sample is drawn from the Texas radio survey with the following primary selection criteria: zem≥ 2.4, optical magnitudes B≤ 22 and 365 MHz flux density S365≥ 400 mJy. We obtained spectra for a sample of 45 quasi-stellar objects (QSOs) with the William Herschel Telescope, Very Large Telescope and Gemini-North, resulting in a survey redshift path Δz= 38.79. We detect nine DLAs and one sub-DLA, with a mean absorption redshift 〈z〉= 2.44. The DLA number density is n(z) = 0.23+0.11−0.07, in good agreement with the value derived for DLAs detected in the Sloan Digital Sky Survey at this redshift. The DLA number density of our sample is also in good agreement with optically complete radio-selected samples, supporting previous claims that n(z) is not significantly affected by dust obscuration bias. We present N(H i) column density determinations and metal line equivalent width measurements for all our DLAs. The low-frequency flux-density selection criterion used for the quasar sample implies that all absorbers will be suitable for follow-up absorption spectroscopy in the redshifted H i 21 cm line. A following paper (Kanekar et al.) will present H i 21 cm absorption studies of, and spin temperature determinations for, our DLA sample.
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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 | 0.002 | 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) | 0.001 | 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".