Oxygen abundance distributions in six late-type galaxies based on SALT spectra of H II regions
Bibliographic record
Abstract
\n Spectra of 34 H ii regions in the late-type galaxies NGC 1087, NGC 2967, NGC 3023, NGC 4030, NGC 4123, and NGC 4517A were observed with the South African Large Telescope (SALT). In all 34 H ii regions, oxygen abundances were determined through the “counterpart” method (C method). Additionally, in two H ii regions in which we detected auroral lines, we measured oxygen abundances with the classic Te method. We also estimated the abundances in our H ii regions using the O3N2 and N2 calibrations and compared those with the C-based abundances. With these data, we examined the radial abundance distributions in the disks of our target galaxies. We derived surface-brightness profiles and other characteristics of the disks (the surface brightness at the disk center and the disk scale length) in three photometric bands for each galaxy using publicly available photometric imaging data. The radial distributions of the oxygen abundances predicted by the relation between abundance and disk surface brightness in the W1 band obtained for spiral galaxies in our previous study are close to the radial distributions of the oxygen abundances determined from the analysis of the emission line spectra for four galaxies where this relation is applicable. Hence, when the surface-brightness profile of a late-type galaxy is known, this parametric relation can be used to estimate the likely present-day oxygen abundance in the disk of the galaxy. \n
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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 | 0.000 |
| 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 | 0.000 |
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".