Updated 34-band Photometry for the SINGS/KINGFISH Samples of Nearby Galaxies
Bibliographic record
Abstract
Abstract We present an update to the ultraviolet-to-radio database of global broadband photometry for the 79 nearby galaxies that comprise the union of the KINGFISH (Key Insights on Nearby Galaxies: A Far-Infrared Survey with Herschel) and SINGS (Spitzer Infrared Nearby Galaxies Survey) samples. The 34-band data set presented here includes contributions from observational work carried out with a variety of facilities including GALEX, SDSS, Pan-STARRS1, NOAO, 2MASS, Wide-Field Infrared Survey Explorer, Spitzer, Herschel, Planck, JCMT, and the VLA. Improvements of note include recalibrations of previously published SINGS BVR C I C and KINGFISH far-infrared/submillimeter photometry. Similar to previous results in the literature, an excess of submillimeter emission above model predictions is seen primarily for low-metallicity dwarf or irregular galaxies. This 33-band photometric data set for the combined KINGFISH+SINGS sample serves as an important multiwavelength reference for the variety of galaxies observed at low redshift. A thorough analysis of the observed spectral energy distributions is carried out in a companion paper.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".