The Canada-UK Deep Submillimeter Survey. IV. The Survey of the 14 Hour Field
Bibliographic record
Abstract
We have used SCUBA to survey an area of ≃ 50 arcmin 2 , detecting 19 sources down to a 3 σ sensitivity limit of ∼3.5 mJy at 850 μm. Monte Carlo simulations have shown that the fluxes of sources in this and similar SCUBA surveys are biased upward by the effects of source confusion and noise, leading to an overestimate by a factor of ∼1.4 in the fraction of the 850 μm background that has been resolved by SCUBA. Once a correction is made for this effect, about 20% of the background has been resolved. The simulations have also been used to quantify the effects of confusion on source positions. Of the 19 SCUBA sources, five are microjansky radio sources, and two are ISO 15 μm sources. The radio/submillmeter flux ratios imply that the dust in these galaxies is being heated by young stars rather than active galactic nuclei. The upper limit to the average 450 μm/850 μm flux ratio implies either that the SCUBA galaxies are at z 2 or, if they are at lower redshifts, that the dust is generally colder than in ULIRGs. We have used simple evolution models to address the major questions about the SCUBA sources: (1) What fraction of the star formation at high redshift is hidden by dust? (2) Does the submillimeter luminosity density reach a maximum at some redshift? (3) If the SCUBA sources are protoellipticals, when exactly did ellipticals form? We show, however, that the observations are not yet good enough to answer these questions. There are, for example, acceptable models in which 10 times as much high-redshift star formation is hidden by dust as is seen at optical wavelengths, but also acceptable ones in which the amount of hidden star formation is less than that seen optically. There are acceptable models in which very little star formation occurred before a redshift of 3 (as might be expected in models of hierarchical galaxy formation), but also ones in which 30% of the stars have formed by this redshift. The key to answering these questions are measurements of the dust temperatures and redshifts of the SCUBA sources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.023 | 0.006 |
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".