[no title]
Bibliographic record
Abstract
Previous studies have shown that the incidence rate of intervening strong Mg II absorbers towards gamma-ray bursts (GRBs) were a factor of 2-4 higher than towards quasars. Exploring the similar sized and uniformly selected legacy data sets XQ-100 and XSGRB, each consisting of 100 quasar and 81 GRB afterglow spectra obtained with a single instrument (VLT/X-shooter), we demonstrate that there is no disagreement in the number density of strong Mg II absorbers with rest-frame equivalent widths W_rλ2796>1 Å towardsGRBs and quasars in the redshift range 0.1 ≲ z ≲ 5. With large and similar sample sizes, and path length coverages of Δz = 57.8 and 254.4 for GRBs and quasars, respectively, the incidences of intervening absorbers are consistent within 1σ uncertainty levels at all redshifts. For absorbers at z < 2.3, the incidence towards GRBs is a factor of 1.5 ± 0.4 higher than the expected number of strong Mg II absorbers in Sloan Digital Sky Survey (SDSS) quasar spectra, while for quasar absorbers observed with X-shooter we find an excess factor of 1.4 ± 0.2 relative to SDSS quasars. Conversely, the incidence rates agree at all redshifts with reported high-spectral-resolution quasar data, and no excess is found. The only remaining discrepancy in incidences is between SDSS Mg II catalogues and high-spectral-resolution studies. The rest-frame equivalent-width distribution also agrees to within 1σ uncertainty levels between the GRB and quasar samples. Intervening strong Mg II absorbers towards GRBs are therefore neither unusually frequent, nor unusually strong. Based on observations collected at the European Southern Observatory, Paranal, Chile, Program ID: 098.A-0055, 097.A-0036, 096.A-0079, 095.B-0811(B), 095.A-0045, 094.A-0134, 093.A-0069, 092.A-0124, 0091.C-0934, 090.A-0088, 089.A-0067, 088.A-0051, 087.A-0055, 086.A-0073, 085.A-0009 and 084.A-0260. XQ-100: 189.A-0424.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.059 | 0.023 |
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".