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Record W1643911192 · doi:10.1017/s0074180900209819

Oxygen Abundances for the Planetary Nebulae in M32, NGC 185, and NGC 205

2003· article· en· W1643911192 on OpenAlexaff
M. G. Richer, M. L. McCall

Bibliographic record

VenueSymposium - International Astronomical Union · 2003
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsYork University
Fundersnot available
KeywordsPlanetary nebulaAstrophysicsPhysicsHubble sequenceGalaxyAstronomyLocal GroupDoubly ionized oxygenStarsDwarf galaxyElliptical galaxySpectral lineEmission spectrum

Abstract

fetched live from OpenAlex

We present spectroscopy of planetary nebulae in M32, NGC 185, and NGC 205. With these new data, spectra exist for 14 planetary nebulae in M32, for 5 planetary nebulae in NGC 185, and 13 planetary nebulae in NGC 205. From these data, oxygen abundances based upon electron temperatures derived from detections of [O III]λ4363 may be derived for 11 planetary nebulae in M32, 5 in NGC 185, and 7 in NGC 205. The mean oxygen abundances and their dispersions based upon these [O III]λ4363 detections are 8.51 ± 0.20, 8.21 ± 0.27, and 8.20 ± 0.24 in M32, NGC 185, and NGC 205, respectively, in the usual 12 + log( O / H ) format. We confirm a previous finding that a significant fraction of the planetary nebulae in M32 have anomalously strong [N II] lines (Stasińska et al. 1998). Seven of the planetary nebulae for which there now exist spectra (half of the total) have I ([N II]λ6584)/ I ( H β) > 1.5. These objects are concentrated towards the centre of M32, though less so than the light. When we combine these new oxygen abundances with extant data, we find that the the same physical processes appear to control the chemical evolution of ellipticals, dwarf spheroidals, and the bulges of spirals. In addition, it is clear that dwarf spheroidals incorporated their metal production into stars much more efficiently than dwarf irregulars, casting more doubt upon an evolutionary link between these two types of dwarf galaxies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.194
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2003
Admission routes1
Has abstractyes

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