Completeness of the NASA/IPAC Extragalactic Database (NED) Local Volume Sample
Bibliographic record
Abstract
Abstract We introduce the NASA/IPAC Extragalactic Database (NED) Local Volume Sample (NED-LVS), a subset of ∼1.9 million objects with distances out to 1000 Mpc. We use UV and IR fluxes available in NED from all-sky surveys to derive physical properties, and estimate the completeness relative to the expected local luminosity density. The completeness relative to near-IR luminosities (which traces a galaxy’s stellar mass) is roughly 100% at D < 30 Mpc and remains moderate (70%) out to 300 Mpc. For brighter galaxies (≳ L * ), NED-LVS is ∼100% complete out to ∼400 Mpc. When compared to other local Universe samples (GLADE and HECATE), all three are ∼100% complete below 30 Mpc. At distances beyond ∼80 Mpc, NED-LVS is more complete than both GLADE and HECATE by ∼10%–20%. NED-LVS is the underlying sample for the NED gravitational-wave follow-up service (NED-GWF), which provides prioritized lists of host candidates for GW events within minutes of alerts issued by the LIGO–Virgo–KAGRA collaboration. We test the prioritization of galaxies in the volume of GW170817 by three physical properties, where we find that both stellar mass and inverse specific star formation rate place the correct host galaxy in the top 10. In addition, NED-LVS can be used for a wide variety of other astrophysical studies: galaxy evolution, star formation, large-scale structure, galaxy environments, and more. The data in NED are updated regularly, and NED-LVS will be updated concurrently. Consequently, NED-LVS will continue to provide an increasingly complete sample of galaxies for a multitude of astrophysical research areas for years to come.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".