ELVES II: early-type satellites phot. & GC data (Carlsten+, 2022)
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
For our primary observational sample, we use results from the ongoing Exploration of Local VolumE Satellites Survey (ELVES). Satellite candidates are detected using deep, wide-field imaging combined with the detection algorithm, specialized for finding low-surface-brightness, diffuse dwarf galaxies, of Carlsten+ (2020ApJ...891..144C) and Greco+ (2018, J/ApJ/857/104). The candidate satellites are confirmed with a variety of distance measurements including archival redshifts and tip of the red-giant-branch (TRGB) distances, but the majority of distances are measured via surface-brightness fluctuations (SBF). The survey uses a mixture of archival Canada-France-Hawaii Telescope (CFHT)/MegaCam imaging and the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys, which includes both the Beijing-Arizona Sky Survey (BASS) and the Dark Energy Camera Legacy Survey (henceforth, these surveys are collectively referred to as DECaLS). We used deeper Gemini (program IDs: FT-2020A-060 and US-2020B-037) or Subaru/HSC data, where available, to measure the dwarf distances via SBF. See Section 2.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.036 | 0.060 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.009 |
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