THE MASS DISTRIBUTION OF STARLESS AND PROTOSTELLAR CORES IN GOULD BELT CLOUDS
Bibliographic record
Abstract
Using data from the SCUBA Legacy Catalogue (850 μm) and Spitzer Space Telescope (3.6–70 μm), we explore dense cores in the Ophiuchus, Taurus, Perseus, Serpens, and Orion molecular clouds. We develop a new method to discriminate submillimeter cores found by Submillimeter Common-User Bolometer Array (SCUBA) as starless or protostellar, using point source photometry from Spitzer wide field surveys. First, we identify infrared sources with red colors associated with embedded young stellar objects (YSOs). Second, we compare the positions of these YSO candidates to our submillimeter cores. With these identifications, we construct new, self-consistent starless and protostellar core mass functions (CMFs) for the five clouds. We find best-fit slopes to the high-mass end of the CMFs of −1.26 ± 0.20, −1.22 ± 0.06, −0.95 ± 0.20, and −1.67 ± 0.72 for Ophiuchus, Taurus, Perseus, and Orion, respectively. Broadly, these slopes are each consistent with the −1.35 power-law slope of the Salpeter initial mass function at higher masses, but suggest some differences. We examine a variety of trends between these CMF shapes and their parent cloud properties, potentially finding a correlation between the high-mass slope and core temperature. We also find a trend between core mass and effective size, but we are very limited by sensitivity. We make similar comparisons between core mass and size with visual extinction (for A V ⩾ 3) and find no obvious trends. We also predict the numbers and mass distributions of cores that future surveys with SCUBA-2 may detect in each of these clouds.
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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 | 0.002 | 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".