The Star Formation Reference Survey. I. Survey Description and Basic Data
Bibliographic record
Abstract
Star formation is arguably the most important physical process in the cosmos. It is a fundamental driver of galaxy evolution and the ultimate source of most of the energy emitted by galaxies. A correct interpretation of star formation rate (SFR) measures is therefore essential to our understanding of galaxy formation and evolution. Unfortunately, however, no single SFR estimator is universally available or even applicable in all circumstances: the numerous galaxies found in deep surveys are often too faint (or too distant) to yield significant detections with most standard SFR measures, and until now there have been no global, multi-band observations of nearby galaxies that span all the conditions under which star-formation is taking place. To address this need in a systematic way, we have undertaken a multi-band survey of all types of star-forming galaxies in the local Universe. This project, the Star Formation Reference Survey (SFRS), is based on a statistically valid sample of 369 nearby galaxies that span all existing combinations of dust temperature, SFR, and specific SFR. Furthermore, because the SFRS is blind with respect to AGN fraction and environment it serves as a means to assess the influence of these factors on SFR. Our panchromatic global flux measurements (including GALEX FUV+NUV, SDSS ugriz, 2MASS JHKs, Spitzer 3-8μm, and others) furnish uniform SFR measures and the context in which their reliability can be assessed. This paper describes the SFRS survey strategy, defines the sample, and presents the multi-band photometry collected to date.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.026 |
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".