PHOTOIONIZED MIXING LAYER MODELS OF THE DIFFUSE IONIZED GAS
Bibliographic record
Abstract
It is generally believed that O stars, confined near the galactic midplane, are somehow able to photoionize a significant fraction of what is termed the "diffuse ionized gas" (DIG) of spiral galaxies, which can extend up to 1–2 kpc above the galactic midplane. The heating of the DIG remains poorly understood, however, as simple photoionization models do not reproduce the observed line ratio correlations well or the DIG temperature. We present turbulent mixing layer (TML) models in which warm photoionized condensations are immersed in a hot supersonic wind. Turbulent dissipation and mixing generate an intermediate region where the gas is accelerated, heated, and mixed. The emission spectrum of such layers is compared with observations of Rand of the DIG in the edge-on spiral NGC 891. We generate two sequence of models that fit the line ratio correlations between [S ii ]/Hα, [O i ]/Hα, [N ii ]/[S ii ], and [O iii ]/Hβ reasonably well. In one sequence of models, the hot wind velocity increases, while in the other, the ionization parameter and layer opacity increase. Despite the success of the mixing layer models, the overall efficiency in reprocessing the stellar UV is much too low, much less than 1%, which compels us to reject the TML model in its present form.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".