THE JCMT GOULD BELT SURVEY: A FIRST LOOK AT DENSE CORES IN ORION B
Bibliographic record
Abstract
ABSTRACT We present a first look at the SCUBA-2 observations of three sub-regions of the Orion B molecular cloud: LDN 1622, NGC 2023/2024, and NGC 2068/2071, from the JCMT Gould Belt Legacy Survey. We identify 29, 564, and 322 dense cores in L1622, NGC 2023/2024, and NGC 2068/2071 respectively, using the SCUBA-2 850 μm map, and present their basic properties, including their peak fluxes, total fluxes, and sizes, and an estimate of the corresponding 450 μm peak fluxes and total fluxes, using the FellWalker source extraction algorithm. Assuming a constant temperature of 20 K, the starless dense cores have a mass function similar to that found in previous dense core analyses, with a Salpeter-like slope at the high-mass end. The majority of cores appear stable to gravitational collapse when considering only thermal pressure; indeed, most of the cores which have masses above the thermal Jeans mass are already associated with at least one protostar. At higher cloud column densities, above 1–2 × 1023 cm−2, most of the mass is found within dense cores, while at lower cloud column densities, below 1 × 1023 cm−2, this fraction drops to 10% or lower. Overall, the fraction of dense cores associated with a protostar is quite small (<8%), but becomes larger for the densest and most centrally concentrated cores. NGC 2023/2024 and NGC 2068/2071 appear to be on the path to forming a significant number of stars in the future, while L1622 has little additional mass in dense cores to form many new stars.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".