The 2dF Galaxy Redshift Survey: the clustering of galaxy groups
Bibliographic record
Abstract
We measure the clustering of galaxy groups in the 2dFGRS Percolation-Inferred Galaxy Group (2PIGG) catalogue. The 2PIGG sample has 28 877 groups with at least two members. The clustering amplitude of the full 2PIGG catalogue is weaker than that of 2dFGRS galaxies, in agreement with theoretical predictions. We have subdivided the 2PIGG catalogue into samples that span a factor of ≈ 25 in median total luminosity. Our correlation function measurements span an unprecedented range of clustering strengths, connecting the regimes probed by groups fainter than L* galaxies and rich clusters. There is a steady increase in clustering strength with group luminosity; the most luminous groups are 10 times more strongly clustered than the full 2PIGG catalogue. We demonstrate that the 2PIGG results are in very good agreement with the clustering of groups expected in the ΛCDM model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".