A Spitzer Space Telescope study of dust features in planetary nebula and H<scp>ii</scp> regions
Bibliographic record
Abstract
Abstract One of the key questions of infrared astronomy is how the characteristics of dust depend on the physical properties of the surrounding medium. To address this question, we present results from the Spitzer Space Telescope on two projects designed to study the dust properties of a sample of 25 Planetary Nebulae (PNe) in the Magellanic Clouds, and three well-known Giant Hii regions (NGC 3603, 30 Doradus and N 66/NGC 346). Most PNe show emission from polycyclic aromatic hydrocarbons (PAHs) and only two of them show amorphous silicates. Eleven PNe display a strong broad feature around 11 μm which is attributed to silicon carbide and 8 of them show magnesium sulfide. One PNe, SMP LMC 11, shows spectacular absorption bands due to molecules which are the precursors from which more complex hydrocarbons are formed. The Spitzer spectra of the Hii regions, NGC 3603, 30 Doradus, and NGC 346 are very rich, displaying a wealth of spectral features within each region. This not only allows us to compare the dust at different metallicities but also to study the spatial variations of many features across a given region and correlate it with the distance to the ionizing cluster(s) and other parameters.
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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 | 0.001 |
| 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) | 0.001 | 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".