GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended (GLEAM-X) I: Survey Description and Initial Data Release
Bibliographic record
Abstract
We describe a new low-frequency wideband radio survey of the southern sky. Observations covering 72 - 231 MHz and Declinations south of $+30^\circ$ have been performed with the Murchison Widefield Array "extended" Phase II configuration over 2018 - 2020 and will be processed to form data products including continuum and polarisation images and mosaics, multi-frequency catalogues, transient search data, and ionospheric measurements. From a pilot field described in this work, we publish an initial data release covering 1,447 sq. deg over 4h < RA < 13h, -32.7deg < Dec < -20.7deg. We process twenty frequency bands sampling 72 - 231 MHz, with a resolution of $2'$ - $45"$, and produce a wideband source-finding image across 170 - 231MHz with a root-mean-square noise of $1.27\pm0.15$ mJy/beam. Source-finding yields 78,967 components, of which 71,320 are fitted spectrally. The catalogue has a completeness of 98% at $\sim50$mJy, and a reliability of 98.2% at $5\sigma$ rising to 99.7% at $7\sigma$. A catalogue is available from Vizier; images are made available on AAO Data Central, SkyView, and the PASA Datastore. This is the first in a series of data releases from the GLEAM-X survey.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.031 |
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".