An Overview of the Dwarf Galaxy Survey
Bibliographic record
Abstract
The Dwarf Galaxy Survey (DGS) program is studying low-metallicity galaxies using 230 hr of far-infrared (FIR) and submillimetre (submm) photometric and spectroscopic observations of the Herschel Space Observatory and draws from this a rich database of a wide range of wavelengths tracing the dust, gas and stars. This sample of 50 galaxies includes the largest metallicity range achievable in the local Universe including the lowest metallicity ( Z ) galaxies, 1/50 Z ⊙ , and spans four orders of magnitude in star formation rates. The survey is designed to get a handle on the physics of the interstellar medium (ISM) of low metallicity dwarf galaxies, especially their dust and gas properties and the ISM heating and cooling processes. The DGS produces PACS and SPIRE maps of low-metallicity galaxies observed at 70, 100, 160, 250, 350, and 500 μm with the highest sensitivity achievable to date in the FIR and submm. The FIR fine-structure lines, [CII] 158 μm, [OI] 63 μm, [OI] 145 μm, [OIII] 88 μm, [NIII] 57 μm, and [NII] 122 and 205 μm have also been observed with the aim of studying the gas cooling in the neutral and ionized phases. The SPIRE FTS observations include many CO lines ( J = 4–3 to J = 13–12), [NII] 205 μm, and [CI] lines at 370 and 609 μm. This paper describes the sample selection and global properties of the galaxies and the observing strategy as well as the vast ancillary database available to complement the Herschel observations. The scientific potential of the full DGS survey is described with some example results included.
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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 | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.041 |
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".