THE Spitzer SURVEY OF INTERSTELLAR CLOUDS IN THE GOULD BELT. VI. THE AURIGA–CALIFORNIA MOLECULAR CLOUD OBSERVED WITH IRAC AND MIPS
Bibliographic record
Abstract
We present observations of the Auriga–California Molecular Cloud (AMC) at 3.6, 4.5, 5.8, 8.0, 24, 70, and 160 μm observed with the IRAC and MIPS detectors as part of the Spitzer Gould Belt Legacy Survey. The total mapped areas are 2.5 deg 2 with IRAC and 10.47 deg 2 with MIPS. This giant molecular cloud is one of two in the nearby Gould Belt of star-forming regions, the other being the Orion A Molecular Cloud (OMC). We compare source counts, colors, and magnitudes in our observed region to a subset of the SWIRE data that was processed through our pipeline. Using color–magnitude and color–color diagrams, we find evidence for a substantial population of 166 young stellar objects (YSOs) in the cloud, many of which were previously unknown. Most of this population is concentrated around the LkHα 101 cluster and the filament extending from it. We present a quantitative description of the degree of clustering and discuss the relative fraction of YSOs in earlier (Class I and F) and later (Class II) classes compared to other clouds. We perform simple SED modeling of the YSOs with disks to compare the mid-IR properties to disks in other clouds and identify 14 classical transition disk candidates. Although the AMC is similar in mass, size, and distance to the OMC, it is forming about 15–20 times fewer 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.001 |
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".