<i>Herschel</i>and SCUBA-2 observations of dust emission in a sample of<i>Planck</i>cold clumps
Bibliographic record
Abstract
Context.Analysis of all-skyPlancksubmillimetre observations and the IRAS 100μm data has led to the detection of a population of Galactic cold clumps. The clumps can be used to study star formation and dust properties in a wide range of Galactic environments. Aims.Our aim is to measure dust spectral energy distribution (SED) variations as a function of the spatial scale and the wavelength. Methods.We examined the SEDs at large scales using IRAS,Planck, andHerscheldata. At smaller scales, we compared JCMT/SCUBA-2 850μm maps withHerscheldata that were filtered using the SCUBA-2 pipeline. Clumps were extracted using the Fellwalker method, and their spectra were modelled as modified blackbody functions. Results.According to IRAS andPlanckdata, most fields have dust colour temperaturesTC~ 14–18 K and opacity spectral index values ofβ= 1.5–1.9. The clumps and cores identified in SCUBA-2 maps haveT~ 13 K and similarβvalues. There are some indications of the dust emission spectrum becoming flatter at wavelengths longer than 500μm. In fits involvingPlanckdata, the significance is limited by the uncertainty of the corrections for CO line contamination. The fits to the SPIRE data give a medianβvalue that is slightly above 1.8. In the joint SPIRE and SCUBA-2 850μm fits, the value decreases toβ~ 1.6. Most of the observedT-βanticorrelation can be explained by noise. Conclusions.The typical submillimetre opacity spectral indexβof cold clumps is found to be ~1.7. This is above the values of diffuse clouds, but lower than in some previous studies of dense clumps. There is only tentative evidence of aT-βanticorrelation andβdecreasing at millimetre wavelengths.
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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".