RESOLVING THE RADIO SOURCE BACKGROUND: DEEPER UNDERSTANDING THROUGH CONFUSION
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
We used the Karl G. Jansky Very Large Array (VLA) to image one primary beam area at 3 GHz with 8 arcsec FWHM resolution and 1.0 microJy/beam rms noise near the pointing center. The P(D) distribution from the central 10 arcmin of this confusion-limited image constrains the count of discrete sources in the 1 < S(microJy/beam) < 10 range. At this level the brightness-weighted differential count S^2 n(S) is converging rapidly, as predicted by evolutionary models in which the faintest radio sources are star-forming galaxies; and ~96$% of the background originating in galaxies has been resolved into discrete sources. About 63% of the radio background is produced by AGNs, and the remaining 37% comes from star-forming galaxies that obey the far-infrared (FIR) / radio correlation and account for most of the FIR background at lambda = 160 microns. Our new data confirm that radio sources powered by AGNs and star formation evolve at about the same rate, a result consistent with AGN feedback and the rough correlation of black hole and bulge stellar masses. The confusion at centimeter wavelengths is low enough that neither the planned SKA nor its pathfinder ASKAP EMU survey should be confusion limited, and the ultimate source detection limit imposed by "natural" confusion is < 0.01 microJy at 1.4 GHz. If discrete sources dominate the bright extragalactic background reported by ARCADE2 at 3.3 GHz, they cannot be located in or near galaxies and most are < 0.03 microJy at 1.4 GHz.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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