Spectroscopy of Moderately High Redshift RCS-1 Clusters
Bibliographic record
Abstract
We present spectroscopic observations of 11 moderately high-redshift ( z ∼ 0.7-1.0) clusters from the first Red-Sequence Cluster Survey (RCS-1). We confirm that at least 10 of the 11 systems represent genuine overdensities in redshift space and show that for the remaining system, the spectroscopy was not deep enough to confirm a cluster. This is in good agreement with the estimated false positive rate of <5% at these redshifts from simulations. We find excellent agreement between the red-sequence-estimated redshift and the spectroscopic redshift, with a scatter of 10% at z > 0.7. At the high-redshift end ( z ≳ 0.9) of the sample, we find that two of the systems selected are projections of pairs of comparably rich systems, with red sequences too close to discriminate in ( R - z ') color. In one of these systems, the two components are close enough to be physically associated. For a subsample of clusters with sufficient spectroscopic members, we examine the correlation between B gcR (optical richness) and the dynamical mass inferred from the velocity dispersion. We find these measurements to be compatible, within the relatively large uncertainties, with the correlation established at lower redshift for the X-ray-selected Canadian Network for Observational Cosmology clusters and also for a lower redshift sample of RCS-1 clusters. Confirmation of this and calibration of the scatter in the relation will require larger samples of clusters at these and higher redshifts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".