Bibliographic record
Abstract
Context.Properties of galaxies, such as their absolute magnitude and stellar mass content, are correlated. These correlations are tighter for close pairs of galaxies, which is called galactic conformity. In hierarchical structure formation scenarios, galaxies form within dark matter haloes. To explain the amplitude and spatial range of galactic conformity two-halo terms or assembly bias become important. Aims.With the scale dependent correlation coefficients, the amplitude and spatial range of conformity are determined from galaxy and halo samples. Methods.The scale dependent correlation coefficients are introduced as a new descriptive statistic to quantify the correlations between properties of galaxies or haloes, depending on the distances to other galaxies or haloes. These scale dependent correlation coefficients can be applied to the galaxy distribution directly. Neither a splitting of the sample into subsamples, nor an a priori clustering is needed. Results.This new descriptive statistic is applied to galaxy catalogues derived from the Sloan Digital Sky Survey III and to halo catalogues from the MultiDark simulations. In the galaxy sample the correlations between absolute magnitude, velocity dispersion, ellipticity, and stellar mass content are investigated. The correlations of mass, spin, and ellipticity are explored in the halo samples. Both for galaxies and haloes a scale dependent conformity is confirmed. Moreover the scale dependent correlation coefficients reveal a signal of conformity out to 40 Mpc and beyond. The halo and galaxy samples show a differing amplitude and range of conformity.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".