Abundances of Planetary Nebulae and Evolved Stars: Iron and Sulfur Depletion, and Carbon and Nitrogen Enrichment, in Low- and Intermediate-mass Stellar Populations in the Milky Way
Bibliographic record
Abstract
Abstract We research the elemental abundances in Galactic planetary nebulae (PNe) compared with those of their stellar progenitors (red giant branch and asymptotic giant branch, AGB, stars), to explore and quantify the expected—i.e., due to AGB evolution or condensation onto grains—differences. We gleaned the current literature for the nebular abundances while we used the APOGEE DR 17 survey data for the stellar sample. We examined the elements in common between the nebular and stellar samples, namely, C, N, O, Fe, and S. We confirm that iron in PNe is mostly entrapped in grains, with an average depletion 〈D[Fe/H]〉 = 1.741 ± 0.486 dex, and we disclose a weak correlation between iron depletion and the [O/H] abundance, D[Fe/H] = (6.6003 ± 2.443) × [O/H] + (1.972 ± 0.199). Sulfur may also be mildly depleted in PNe, with 〈D[S/H]〉 = 0.179 ± 0.291 dex. We also found an indication of nitrogen enrichment for PNe 〈E[N/H]〉 = 0.393 ± 0.421 dex, with maximum enrichment (0.980 ± 0.243) occurring for the PNe whose progenitors have gone through the hot-bottom burning. The carbon enrichment is 〈E[C/H]〉 = 0.337 ± 0.463 dex when measured for the general PN populations. Our results will be relevant for future Galactic and extragalactic studies comparing nebular and stellar samples.
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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 | 0.001 | 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".