The JCMT Gould Belt Survey: radiative heating by OB stars
Bibliographic record
Abstract
ABSTRACT Radiative feedback can influence subsequent star formation. We quantify the heating from OB stars in the local star-forming regions in the James Clerk Maxwell Telescope (JCMT) Gould Belt Survey. Dust temperatures are calculated from 450/850 $\mu$m flux ratios from SCUBA-2 observations at the JCMT assuming a fixed dust opacity spectral index β = 1.8. Mean dust temperatures are calculated for each submillimetre clump along with projected distances from the main OB star in the region. Temperature versus distance is fitted with a simple model of dust heating by the OB star radiation plus the interstellar radiation field and dust cooling through optically thin radiation. Classifying the heating sources by spectral type, O-type stars produce the greatest clump average temperature rises and largest heating extent, with temperatures of over 40 K and significant heating out to at least 2.4 pc. Early-type B stars (B4 and above) produce temperatures of over 20 K and significant heating over 0.4 pc. Late-type B stars show a marginal heating effect within 0.2 pc. For a given projected distance, there is a significant scatter in clump temperatures that is due to local heating by other luminous stars in the region, projection effects, or shadowing effects. Even in these local, ‘low-mass’ star-forming regions, radiative feedback is having an effect on parsec scales, with 24 per cent of the clumps heated to at least 3 K above the 15 K base temperature expected from heating by only the interstellar radiation field, and a mean dust temperature for heated clumps of 24 K.
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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".