A<i>SPITZER</i>SURVEY OF PROTOPLANETARY DISK DUST IN THE YOUNG SERPENS CLOUD: HOW DO DUST CHARACTERISTICS EVOLVE WITH TIME?
Bibliographic record
Abstract
We present Spitzer IRS mid-infrared (5-35 micron) spectra of a complete flux-limited sample (> 3 mJy at 8 micron) of young stellar object (YSO) candidates selected on the basis of their infrared colors in the Serpens Molecular Cloud. Spectra of 147 sources are presented and classified. Background stars (with slope consistent with a reddened stellar spectrum and silicate features in absorption), galaxies (with redshifted PAH features) and a planetary nebula (with high ionization lines) amount to 22% of contamination in this sample, leaving 115 true YSOs. Sources with rising spectra and ice absorption features, classified as embedded Stage I protostars, amount to 18% of the sample. The remaining 82% (94) of the disk sources are analyzed in terms of spectral energy distribution shapes, PAHs and silicate features. The presence, strength and shape of these silicate features are used to infer disk properties for these systems. About 8% of the disks have 30/13 micron flux ratios consistent with cold disks with inner holes or gaps, and 3% of the disks show PAH emission. Comparison with models indicates that dust grains in the surface of these disks have sizes of at least a few \mu\m. The 20 micron silicate feature is sometimes seen in absence of the 10 micron feature, which may be indicative of very small holes in these disks. No significant difference is found in the distribution of silicate feature shapes and strengths between sources in clusters and in the field. Moreover, the results in Serpens are compared with other well-studied samples: the c2d IRS sample distributed over 5 clouds and a large sample of disks in the Taurus star-forming region. The remarkably similar distributions of silicate feature characteristics in samples with different environment and median ages - if significant - imply that the dust population in the disk surface results from an equilibrium between dust growth and destructive collision processes that are maintained over a few million years for any YSO population irrespective of environment.
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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 | 0.001 | 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".