A wide-area GMRT 610-MHz survey of ELAIS N1 field
Bibliographic record
Abstract
ABSTRACT In this paper, we present a wide-area 610-MHz survey of the ELAIS N1 field with the GMRT, covering an area of 12.8 deg2 at a resolution of 6 arcsec and with an rms noise of ∼40 $\mu$Jy beam−1. This is equivalent to ∼20 $\mu$Jy beam−1 rms noise at 1.4 GHz for a spectral index of −0.75. The primary goal of the survey was to study the polarized sky at sub-mJy flux densities at < GHz frequencies, alongwith a range of other science goals such as investigations into the nature of the low-frequency $\mu$Jy source populations and alignments of radio jets. A total of 6400 sources were found in this region, the vast majority of them compact. The sample jointly detected by GMRT at 610 MHz and by VLA FIRST at 1.4 GHz has a median spectral index of −0.85 ± 0.05 and a median 610-MHz flux density of 4.5 mJy. This region has a wealth of ancillary data, which is useful to characterize the detected sources. The multiwavelength crossmatching resulted optical/IR counterparts to ∼90 per cent of the radio sources, with a significant fraction having at least photometric redshift. Due to the improved sensitivity of this survey over preceding ones, we have discovered six giant radio sources (GRSs), with three of them at z ∼ 1 or higher. This implies that the population of GRS may be more abundant and common than known to date and if true, this has implications for the luminosity function and the evolution of radio sources. We have also identified several candidate-extended relic sources.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".