A-type Stellar Abundances: A Corollary to Herschel Observations of Debris Disks
Bibliographic record
Abstract
Abstract In order to assess the relationship between metallicity and exoplanetary systems, we compare the abundances of AF-type main-sequence stars with debris disk properties assessed using Herschel observations of an unbiased survey of nearby stars. Hot stars are not as commonly observed, given their unique constraints in data reduction, lack of metal lines, and “astrophysical noise” from rotation speed. Here, we address that deficiency using new and archival spectra of 83 AF-type stars. We measure the abundances of a few species in addition to Fe in order to classify the stars with Ap/Am or Lambda Boo signatures. Lambda Boo stars have a chemical signature of solar-abundant volatile species and sub-solar refractory abundances that is hypothesized to be altered by the pollution of volatiles. Overall, we see no correlation between debris disks and metallicity, primarily because the sample size is cut significantly when using only reliable fits to the spectroscopic data. The abundance measured from the Mg ii 4481 blend is a useful diagnostic because it can be reliably measured at large v·sin(i) and is found to be lower around stars with bright debris disks. We find that Lambda Boo stars have brighter debris disks compared to a bias-free sample of AF stars. The trend with disk brightness and Mg abundances suggests pollution effects can be significant and used as a marker for the stability of planetary systems. We explore trends with other species, such as with the C/O ratios, but are significantly limited by the low number of reliable detections.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".