Comparing NED and SIMBAD classifications across the contents of nearby galaxies
Bibliographic record
Abstract
ABSTRACT Cataloguing and classifying celestial objects is one of the fundamental activities of observational astrophysics. In this work, we compare the contents of two comprehensive data bases, the NASA Extragalactic Database (NED) and Set of Identifications, Measurements and Bibliography for Astronomical Data (SIMBAD) in the vicinity of nearby galaxies. These two data bases employ different classification schemes – one flat and one hierarchical – and our goal was to determine the compatibility of classifications for objects in common. Searching both data bases for objects within the respective isophotal radius of each of the ∼1300 individual galaxies in the Local Volume Galaxy sample, we found that, on average, NED contains about 10 times as many entries as SIMBAD and about two-thirds of SIMBAD objects are matched by position to a NED object, at 5 arcsec tolerance. These quantities do not depend strongly on the properties of the parent galaxies. We developed an algorithm to compare individual object classifications between the two data bases and found that 88 per cent of the classifications agree; we conclude that NED and SIMBAD contain consistent information for sources in common in the vicinity of nearby galaxies. Because many galaxies have numerous sources contained only in one of NED or SIMBAD, researchers seeking the most complete picture of an individual galaxy’s contents are best served by using both data bases.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".