The JCMT Gould Belt Survey: A First Look at the Auriga–California Molecular Cloud with SCUBA-2
Bibliographic record
Abstract
Abstract We present 850 and 450 μm observations of the dense regions within the Auriga–California molecular cloud using SCUBA-2 as part of the JCMT Gould Belt Legacy Survey to identify candidate protostellar objects, measure the masses of their circumstellar material (disk and envelope), and compare the star formation to that in the Orion A molecular cloud. We identify 59 candidate protostars based on the presence of compact submillimeter emission, complementing these observations with existing Herschel/SPIRE maps. Of our candidate protostars, 24 are associated with young stellar objects (YSOs) in the Spitzer and Herschel/PACS catalogs of 166 and 60 YSOs, respectively (177 unique), confirming their protostellar nature. The remaining 35 candidate protostars are in regions, particularly around LkHα 101, where the background cloud emission is too bright to verify or rule out the presence of the compact 70 μm emission that is expected for a protostellar source. We keep these candidate protostars in our sample but note that they may indeed be prestellar in nature. Our observations are sensitive to the high end of the mass distribution in Auriga–Cal. We find that the disparity between the richness of infrared star-forming objects in Orion A and the sparsity in Auriga–Cal extends to the submillimeter, suggesting that the relative star formation rates have not varied over the Class II lifetime and that Auriga–Cal will maintain a lower star formation efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".