<scp>rescuer</scp>: cosmological <i>K</i>-corrections for star clusters
Bibliographic record
Abstract
ABSTRACT The advent of JWST now allows entire star cluster populations to be imaged in galaxies at cosmologically significant redshifts, bringing with it the need to apply K-corrections to their magnitudes and colour indices. Since the stellar populations within star clusters can be well approximated by a single age and metallicity, their spectral energy distributions are very different from those of galaxies or supernovae, and their K-corrections behave differently. We derive the photometric K-corrections versus redshift for model star clusters that cover a wide range of ages and metallicities, illustrating the results particularly for the broad-band filters on the Hubble Space Telescope/Advanced Camera for Surveys and the JWST/Near-Infrared Camera that are most commonly being used for imaging of populations of star clusters in distant galaxies. In an appendix, we introduce a webtool called rescuer (REdshifted Star ClUstERs) that can generate K-values and their uncertainties for any user-defined combination of cluster properties.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.020 |
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".