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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".