H I content in Coma cluster substructure
Bibliographic record
Abstract
Context. Galaxy clusters are some of largest structures in the universe. These very dense environments tend to be home to higher numbers of evolved galaxies than found in lower-density environments. It is well known that dense environments can influence the evolution of galaxies through the removal of the neutral gas (H I ) reservoirs that fuel star formation. It is unclear which environment has a stronger effect: the local environment (i.e., the substructure within the cluster), or the cluster itself. Aims. Using the new H I data from the Westerbork Coma Survey, we explore the average H I content of galaxies across the cluster comparing galaxies that reside in substructure to those that do not. Methods. We applied the Dressler–Shectman test to our newly compiled redshift catalogue of the Coma cluster to search for substructure. With so few of the Coma galaxies directly detected in H I , we used the H I stacking technique to probe the average H I content below what can be directly detected. Results. Using the Dressler–Shectman test, we find 15 substructures within the footprint of the Westerbork Coma Survey. We compare the average H I content for galaxies within substructure to those not in substructure. Using the H I stacking technique, we find that those Coma galaxies not detected in H I are more than 10–50 times more H I deficient than expected, which supports the scenario of an extremely efficient and rapid quenching mechanism. By studying the galaxies that are not directly detected in H I , we also find Coma to be more H I deficient than previously thought.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".