A Quick Look at the 3 GHz Radio Sky. I. Source Statistics from the Very Large Array Sky Survey
Bibliographic record
Abstract
Abstract The Very Large Array Sky Survey (VLASS) is observing the entire sky north of −40° in the S band (2 GHz < ν < 4 GHz), with the highest angular resolution (2.″5) of any all-sky radio continuum survey to date. VLASS will cover its entire footprint over three distinct epochs, the first of which has now been observed in full. Based on Quick Look images from this first epoch, we have created a catalog of 1.9 × 106 reliably detected radio components. Due to the limitations of the Quick Look images, component flux densities are underestimated by ∼15% at S peak > 3 mJy beam−1 and are often unreliable for fainter components. We use this catalog to perform statistical analyses of the ν ∼ 3 GHz radio sky. Comparisons with the Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) survey show the typical 1.4–3 GHz spectral index, α, to be ∼−0.71. The radio color–color distribution of point and extended components is explored by matching with FIRST and the LOFAR Two-meter Sky Survey. We present the VLASS source counts, dN/dS, which are found to be consistent with previous observations at 1.4 and 3 GHz. Resolution improvements over FIRST result in excess power in the VLASS two-point correlation function at angular scales ≲7″, and in 18% of active galactic nuclei associated with a single FIRST component being split into multicomponent sources by VLASS.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".