Submillimetre photometry of typical high-redshift radio quasars
Bibliographic record
Abstract
We present Submillimetre Common-User Bolometer Array (SCUBA) photometry of a sample of eight high-redshift (2.5 ≤z < 3.5) radio quasars from two redshift surveys: the TexOx-1000 (or TOOT) Survey and the 7C Redshift Survey (7CRS). Unlike the powerful high-redshift radio sources observed previously in the submillimetre, these radio sources are typical of those dominating the radio luminosity density of the population. We detect just two of the TOOT/7CRS targets at 850 μm, and one of these detections is probably due to synchrotron emission rather than dust. The population represented by the other six objects is detected in a statistical sense with their average 850-μm flux density implying that they are similar to low-redshift, far-infrared luminous quasars undergoing at most moderate (≲200 M⊙yr−1) starbursts. By considering all the SCUBA data available for radio sources, we conclude that positive correlations between rest-frame far-infrared luminosity LFIR, 151-MHz luminosity L151 and redshift z, although likely to be present, are hard to interpret because of subtle selection and classification biases, small number statistics and uncertainties concerning synchrotron contamination and k-correction. We argue that there is not yet any compelling evidence for significant differences in the submillimetre properties of radio-loud and radio-quiet quasars at high redshift.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".