Bibliographic record
Abstract
We compare the metallicities of stars with radial velocity planets to the metallicity of a sample of field dwarfs. We confirm recent work indicating that the stars-with-planet sample as a whole is iron rich. However, the lowest mass stars tend to be iron poor, with several having [Fe/H]<-0.2, demonstrating that high metallicity is not required for the formation of short period Jupiter-mass planets. We show that the average [Fe/H] increases with increasing stellar mass (for masses below 1.25 solar masses) in both samples, but that the increase is much more rapid in the stars-with-planet sample. The variation of metallicity with stellar age also differs between the two samples. We examine possible selection effects related to variations in the sensitivity of radial velocity surveys with stellar mass and metallicity, and identify a color cutoff (B-V>0.48) that contributes to but does not explain the mass-metallicity trend in the stars-with-planets sample. We use Monte Carlo models to show that adding an average of 6.5 Earth masses of iron to each star can explain both the mass-metallicity and the age-metallicity relations of the stars-with-planets sample. However, for at least one star, HD 38529, there is good evidence that the bulk metallicity is high. We conclude that the observed metallicities and metallicity trends are the result of the interaction of three effects; accretion of about 6 Earth masses of iron rich material, selection effects, and in some cases, high intrinsic metallicity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.002 |
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; both teacher heads agree on what is shown here.
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".