The Large Early Galaxy Astrophysics Census (LEGA-C) Data Release 3: 3000 High-quality Spectra of K <sub> s </sub>-selected Galaxies at z > 0.6
Bibliographic record
Abstract
Abstract We present the third and final data release of the Large Early Galaxy Astrophysics Census (LEGA-C), an ESO/Very Large Telescope public spectroscopic survey targeting 0.6 < z < 1.0, K s -selected galaxies. The data release contains 3528 spectra with measured stellar velocity dispersions and stellar population properties, a 25-fold increase in sample size compared to previous work. This K s -selected sample probes the galaxy population down to ∼0.3 L *, for all colors and morphological types. Along with the spectra, we publish a value-added catalog with stellar and ionized gas velocity dispersions, stellar absorption line indices, emission-line fluxes, and equivalent widths, complemented with structural parameters measured from Hubble Space Telescope Advanced Camera for Surveys imaging. With its combination of high precision and large sample size, LEGA-C provides a new benchmark for galaxy evolution studies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".