Using Numerical Simulations to Gain Insight into the Structure of Superbubbles
Bibliographic record
Abstract
. Recent high resolution observations of Galactic superbubbles have motivated us to re-examine several classes of superbubble models. We compare three classes of hydrodynamic models (the Kompaneets approximation, the thin shell model, and numerical simulations) in order to understand the structure of superbubbles and to gain insight into observations. In particular, we apply models to the W4 superbubble, which has been observed in the Pilot project of the arcminute resolution Canadian Galactic Plane Survey (Normandeau et al. 1996). Magnetohydrodynamic simulations are also performed and point the way to a fuller understanding of the W4 superbubble. We suggest that the highly collimated bubble and apparent lack of a Rayleigh-Taylor instability in the superbubble shell can be explained by the presence of a magnetic field. 1. Introduction In a recent paper, Basu, Johnstone, & Martin (1999) have modeled the shape and ionization structure of the W4 superbubble (see Normandeau & Basu, these...
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".