THE LAST OF<i>FIRST</i>: THE FINAL CATALOG AND SOURCE IDENTIFICATIONS
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
The FIRST survey, begun over twenty years ago, provides the definitive high-resolution map of the radio sky. This VLA survey reaches a 20cm detection sensitivity of 1 mJy over 10,575 deg**2 largely coincident with the SDSS area. Images and a catalog containing 946,432 sources are available through the FIRST web site (http://sundog.stsci.edu). We record here the authoritative survey history, including hardware and software changes that affect the catalog's reliability and completeness. We use recent JVLA observations to test the survey astrometry and flux bias/scale. Our sidelobe-flagging algorithm finds that fewer than 10% of the catalogued objects are likely sidelobes; these are faint sources concentrated near bright sources, as expected. A match with the NRAO VLA Sky Survey shows very good consistency in flux scale and astrometry. Matches with 2MASS and SDSS indicate a systematic 10-20mas astrometric error with respect to the optical reference frame in old VLA data that has disappeared with the advent of the JVLA. We demonstrate strikingly different behavior between the radio matches to stellar objects and to galaxies in the optical and IR surveys reflecting the different radio populations present over the flux density range 1-1000 mJy. As the radio flux density declines, quasars get redder and fainter, while galaxies get brighter and have colors that initially redden but then turn bluer near the FIRST detection limit. Implications for future radio sky surveys are also discussed. In particular, we show that for radio source identification at faint optical magnitudes, high angular resolution observations are essential, and cannot be sacrificed in exchange for high signal-to-noise data. The value of a JVLA survey as a complement to SKA precursor surveys is briefly discussed.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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