Cores in infrared dark clouds (IRDCs) seen in the Hi-GAL survey between l= 300° and 330°
Bibliographic record
Abstract
We have used data taken as part of the Herschel infrared Galactic Plane survey (Hi-GAL) to study 3171 infrared dark cloud (IRDC) candidates that were identified in the mid-IR (8 μm) by Spitzer (we refer to these as ‘Spitzer-dark’ regions). They all lie in the range l= 300–330° and |b|≤ 1°. Of these, only 1205 were seen in emission in the far-IR (250–500 μm) by Herschel (we call these ‘Herschel-bright’ clouds). It is predicted that a dense cloud will not only be seen in absorption in the mid-IR, but will also be seen in emission in the far-IR at the longest Herschel wavebands (250–500 μm). If a region is dark at all wavelengths throughout the mid-IR and far-IR, then it is most likely to be simply a region of lower background IR emission (a ‘hole in the sky’). Hence, it appears that previous surveys, based on Spitzer and other mid-IR data alone, may have overestimated the total IRDC population by a factor of ∼2. This has implications for estimates of the star formation rate in IRDCs in the Galaxy. We studied the 1205 Herschel-bright IRDCs at 250 μm and found that 972 of them had at least one clearly defined 250-μm peak, indicating that they contained one or more dense cores. Of these, 653 (67 per cent) contained an 8-μm point source somewhere within the cloud, 149 (15 per cent) contained a 24-μm point source but no 8-μm source and 170 (18 per cent) contained no 24- or 8-μm point sources. We use these statistics to make inferences about the lifetimes of the various evolutionary stages of IRDCs.
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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 | 0.001 |
| 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".