Elemental abundance analyses with Coudé Echelle spectrograms from the TÜBİTAK National Observatory of Turkey: I. The HgMn stars 11 Per, HR 2801, and ν Cnc
Bibliographic record
Abstract
Abstract Using coadded spectrograms taken with the Coudé Echelle Spectrograph (CES) of the 1.50‐m Russian‐Turkish Telescope of the TÜBİTAK National Observatory (TUG) near Antalya in Turkey, elemental abundance analyses of three HgMn stars 11 Per, HR 2801, and ν Cnc were performed. Comparisons are made with spectra obtained with the long camera of the Dominion Astrophysical Observatory (DAO) coudé spectrograph and its SITe4 CCD. The CES equivalent widths are about 12% larger than that for the DAO long camera. Our first results from TUG data/spectra show that all three stars exhibit the Hg II λ3984 line and somewhat diverse abundance patterns. 11 Per tends to have underabundant light elements with underabundant and overabundant Fe‐peak elements. HR 2801 has mostly underabundant elements, with a few elements having solar abundances while N and Mn are overabundant. The coolest star ν Cnc has light elements having mostly solar abundances, overabundant iron group abundances, and very overabundant SrYZr and Ba. Comparisons with the abundance patterns of other HgMn stars show that they have a wide diversity of abundance patterns (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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 | 0.002 | 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) | 0.002 | 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".