High-resolution abundance analysis of HD 140283
Bibliographic record
Abstract
Context. HD 140283 is a reference subgiant that is metal poor and confirmed to be a very old star. The element abundances of this type of old star can constrain the nature and nucleosynthesis processes that occurred in its (even older) progenitors. The present study may shed light on nucleosynthesis processes yielding heavy elements early in the Galaxy. Aims. A detailed analysis of a high-quality spectrum is carried out, with the intent of providing a reference on stellar lines and abundances of a very old, metal-poor subgiant. We aim to derive abundances from most available and measurable spectral lines. Methods. The analysis is carried out using high-resolution (R = 81 000) and high signal-to-noise ratio (800 i, O i, Na i, Mg i, Al i, K i, Ca i, Sr ii, and Ba ii lines. Results. The abundance analysis provided an extensive line list suitable for metal-poor subgiant stars. The results for Li, CNO, α-, and iron peak elements are in good agreement with literature. The newly NLTE Ba abundance, along with a NLTE Eu correction and a 3D Ba correction from literature, leads to [Eu/Ba] = + 0.59 ± 0.18. This result confirms a dominant r-process contribution, possibly together with a very small contribution from the main s-process, to the neutron-capture elements in HD 140283. Overabundances of the lighter heavy elements and the high abundances derived for Ba, La, and Ce favour the operation of the weak r-process in HD 140283.
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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 | 0.001 |
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".