The local star formation rate density: assessing calibrations using [O ii], H and UV luminosities
Bibliographic record
Abstract
We explore the use of simple star formation rate (SFR) indicators (such as may be used in high-redshift galaxy surveys) in the local Universe using [O ii], H and u-band luminosities from the deeper 275 deg2 Stripe 82 subsample of the Sloan Digital Sky Survey (SDSS) coupled with UV data from the Galaxy Evolution EXplorer (GALEX) satellite. We examine the consistency of such methods using the star formation rate density as a function of stellar mass in this local volume, and quantify the accuracy of corrections for dust and metallicity on the various indicators. Rest-frame u-band promises to be a particularly good SFR estimator for high-redshift studies since it does not require a particularly large or sensitive extinction correction, yet yields results broadly consistent with more observationally expensive methods. We suggest that the [O ii]-derived SFR, commonly used at higher redshifts , can be used to reliably estimate SFRs for ensembles of galaxies, but for high-mass galaxies , a larger correction than is typically used is required to compensate for the effects of metallicity dependence and dust extinction. We provide a new empirical mass-dependent correction for the [O ii]-SFR.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".