The Herschel Reference Survey
Bibliographic record
Abstract
The Herschel Reference Survey is a Herschel guaranteed time key project and will be a benchmark study of dust in the nearby universe. The survey will complement a number of other Herschel key projects including large cosmological surveys that trace dust in the distant universe. We will use Herschel to produce images of a statistically-complete sample of 323 galaxies at 250, 350, and 500 μm. The sample is volume-limited, containing sources with distances between 15 and 25 Mpc and flux limits in the K band to minimize the selection effects associated with dust and with young high-mass stars and to introduce a selection in stellar mass. The sample spans the whole range of morphological types (ellipticals to late-type spirals) and environments (from the field to the center of the Virgo Cluster) and as such will be useful for other purposes than our own. We plan to use the survey to investigate (i) the dust content of galaxies as a function of Hubble type, stellar mass, and environment; (ii) the connection between the dust content and composition and the other phases of the interstellar medium; and (iii) the origin and evolution of dust in galaxies. In this article, we describe the goals of the survey, the details of the sample and some of the auxiliary observing programs that we have started to collect complementary data. We also use the available multifrequency data to carry out an analysis of the statistical properties of the sample.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.048 |
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".