The VIMOS-VLT Deep Survey
Bibliographic record
Abstract
We measure the evolution of clustering for galaxies with different spectral types from 6495 galaxies with and measured spectroscopic redshifts in the first epoch VIMOS-VLT Deep Survey (VVDS). We divide our sample into four classes, based on the fit of well-defined galaxy spectral energy distributions on observed multi-color data. We measure the projected correlation function and estimate the best-fit parameters for a power-law real-space correlation function . We find the clustering of early-spectral-type galaxies to be markedly stronger than that of late-type galaxies at all redshifts up to . At , early-type galaxies display a correlation length h-1 Mpc, while late types have h-1 Mpc. For the latest class of star-forming blue galaxies, we are able to push our clustering measurement to an effective redshift , for luminous galaxies (). The clustering of these objects increases up to h-1 Mpc for . The relative bias between early- and late-type galaxies within our magnitude-limited survey remains approximately constant with from to . This result is in agrement with the local findings and fairly robust against different way of classifying red and blue galaxies. When compared to the expected linear growth of mass fluctuations, a natural interpretation of these observations is that: (a) the assembly of massive early type galaxies is already mostly complete in the densest dark matter halos at ; (b) luminous late-type galaxies are located in higher-density, more clustered regions of the Universe at than their local low luminous counterpart, indicating that star formation activity is progressively increasing, going back in time, in the higher-density peaks that today are mostly dominated by old galaxies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".