The dust emissivity index β in infrared-bright galaxies at 1.5 &lt; <i>z</i> &lt; 4.2
Bibliographic record
Abstract
ABSTRACT We have measured the dust emissivity index $\beta$ for 21 infrared-bright sources (including several gravitationally lensed galaxies) at $1.5 < z < 4.2$ using Atacama Large Millimetre/submillimetre Array 101–199 GHz data sampling the Rayleigh–Jeans side of the spectral energy distribution. These data are largely insensitive to temperature variations and therefore should provide robust measurements of $\beta$. We obtain a mean $\beta$ of 2.2 with a standard deviation of 0.6 that is at the high end of the range of values that had previously been measured in many galactic and extragalactic sources. We find no systematic variation in $\beta$ versus redshift. We also demonstrate with a subset of our sources that these higher $\beta$ values have significant implications for modelling dust emission and in particular for calculating dust masses or the wavelength at which dust becomes optically thick.
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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) | 0.001 | 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".