Bibliographic record
Abstract
Abstract As the disk formation mechanism(s) in Be stars is(are) as yet unknown, we investigate the role of rapidly rotating radiation-driven winds in this process. We implemented the effects of high stellar rotation on m-CAK models accounting for the shape of the star, the oblate finite disk correction factor, and gravity darkening. For a fast rotating star, we obtain a two-component wind model, i.e., a fast, thin wind in the polar latitudes and an Ω-slow, dense wind in the equatorial regions. We use the equatorial mass densities to explore Hα emission profiles for the following scenarios: (1) a spherically symmetric star, (2) an oblate star with constant temperature, and (3) an oblate star with gravity darkening. One result of this work is that we have developed a novel method for solving the gravity-darkened, oblate m-CAK equation of motion. Furthermore, from our modeling we find that (a) the oblate finite disk correction factor, for the scenario considering the gravity darkening, can vary by at least a factor of two between the equatorial and polar directions, influencing the velocity profile and mass-loss rate accordingly, (b) the Hα profiles predicted by our model are in agreement with those predicted by a standard power-law model for following values of the line-force parameters: , and , and (c) the contribution of the fast wind component to the Hα emission line profile is negligible; therefore, the line profiles arise mainly from the equatorial disks of Be stars.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".