The Canada‐UK Deep Submillimeter Survey. VII. Optical and Near‐Infrared Identifications for the 14 Hour Field
Bibliographic record
Abstract
We present the multiwavelength identifications for 23 sources in the Canada-UK Deep Submillimeter Survey (CUDSS) 14h field. The identifications have been selected on the basis of radio and near-infrared data, and we argue that, to our observational limits, both are effective at selecting the correct counterparts of the SCUBA sources. We discuss the properties of these identifications and find that they are very red in near-infrared color, with many classified as extremely red objects, and show disturbed morphologies. Using the entire CUDSS catalog of 50 sources, we use a combination of spectroscopic redshifts (four objects), 1.4 GHz : 850 μm flux ratio redshift estimates (10 objects), and redshift lower limits based on nondetections at 1.4 GHz (the rest of the sample) to estimate a lower limit on the median redshift of the population of zmed > 1.4. Working from simple models and using the properties of the secure identifications, we discuss general and tentative constraints on the redshift distribution and the expected colors and magnitudes of the entire population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".