Elemental abundance analyses with DAO spectrograms: XXXIII. <i>β</i> UMa (A0mA1 IV‐V), <i>α</i> Dra (A0 III), <i>π</i> Dra (A2 IIIs), and <i>κ</i> Cep (B9 III)
Bibliographic record
Abstract
Abstract This paper presents extended analyses of β UMa (A0mA1 IV‐V), α Dra (A0 III), π Dra (A2 IIIs), and κ Cep (B9 III) which have previously been studied in this series. α Dra is a metal‐poor star while κ Cep has solar abundances. Both β UMa and π Dra are Am stars. Whenever possible, more accurate and precise gf values replace older values. High S/N (200+) and high dispersion Dominion Astrophysical Observatory spectrograms to the red of previously obtained spectra supplement the observations. The derived rotational velocities are 45, 25, 26, and 23 km s–1, respectively. These LTE fine analyses use the ATLAS9 and the WIDTH9 programs of R. L. Kurucz. The results of the extended and the previous analyses are in good agreement. Thus in the past decade a significant improvement in the system of gf values has not been achieved although for many lines there have been changes. The use of additional regions has increased the quality of some results (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".