Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it