Bibliographic record
Abstract
\n We report 250 GHz (1.2 mm) observations of a sample of 20 quasars at redshifts 5.8 < z < 6.5 from the Canada-France High-z Quasar Survey (CFHQS), using the Max-Planck-Millimeter-Bolometer (MAMBO) array at the 30-metre telescope of the Institut de Radioastronomie Millmétrique (IRAM). An rms sensitivity of ≲0.6 mJy was achieved for 65% of the sample, and of ≲1.0 mJy for 90%. Only one quasar, CFHQS J142952+544717, was robustly detected with S250 GHz = 3.46 ± 0.52 mJy. This indicates that one of the most powerful known starbursts at z ~ 6 is associated with this radio-loud quasar. On average, the other CFHQS quasars, which have a mean optical magnitude fainter than the previously studied samples of z ~ 6 quasars of the Sloan Digital Sky Survey (SDSS), have a mean 1.2 mm flux density ⟨ S250 GHz ⟩ = 0.41 ± 0.14 mJy; this average detection with a signal-to-noise (S/N) ratio of 2.9 is hardly meaningful. It would correspond to ⟨ LFIR ⟩ ≈ 0.94 ± 0.32 × 1012 L⊙, and an average star formation rate of a few 100 M⊙/yr, depending on the stellar initial mass function (IMF) and a possible contribution of an active galactic nucleus (AGN) to ⟨ LFIR ⟩. This is consistent with previous findings of Wang et al. on the far-infrared emission of z ~ 6 quasars and extends their results toward optically fainter sources.\n
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".