Improved oscillator strengths and wavelengths in Hf II, with applications to stellar elemental abundances
Bibliographic record
Abstract
Aims. We present new and improved radiative lifetimes for eight levels in and 18 levels in , along with oscillator strengths and wavelengths for 195 transitions in . With these data we determine the abundance of hafnium in two chemically peculiar stars: the hot-Am star HR 3383 and the HgMn star χ Lupi, and discuss the implications of the new data to the hafnium abundance for the Sun and the metal-poor galactic halo stars CS 22892-052 and CS 31082-001. Methods. The oscillator strengths are derived by combining radiative lifetimes measured with the laser induced fluorescence technique and branching fractions determined from intensity calibrated Fourier transform spectra. The hafnium abundance in the two sharp-lined peculair stars is determined by comparison of spectra obtained from instruments onboard the Hubble Space Telescope with synthetic spectra, while the abundance of hafnium in the solar photosphere and the metal-poor halo stars is discussed in terms of rescaling previous investigations using the new gf values. Results. The abundance enhancement of hafnium has been determined in HR 3383 to be +1.7 dex and that for χ Lupi A is +1.3 dex. In the course of the analysis we have also determined an abundance enhancement for molybdenum in HR 3383 to be +1.2 dex, which is similar to that known for χ Lupi A. The abundances in the metal-poor halo stars CS 31082-001 and CS 22892-052 were rescaled to log and -0.82 respectively, with smaller 1σ uncertainty. This has the effect of improving the theoretical fits of r-process nucleosynthesis to abundance data for heavy elements. The change of gf values also implies that the hafnium abundance in the solar photosphere should be reduced by up to 0.2 dex, thereby inducing a discrepancy with the meteoritic hafnium abundance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".