Bibliographic record
Abstract
In this work I investigate the influence of rotation on pulsation frequencies in upper main sequence stars. I use 2D stellar structure models and a 2D linear adiabatic pulsation code to calculate pulsation frequencies for both uniformly and differentially rotating 10 [Special characters omitted.] ZAMS models. Current techniques for these calculations often assume that the pulsation mode can be modelled using a single spherical harmonic and that the rotation rate is slow enough for second order perturbation theory approaches to be valid. These techniques require the rotation rate to be small enough to be considered a small linear perturbation. Using my 2D models, I am able to determine independent limits on the rotation rates for which these techniques are valid. These limits depend strongly on the mode and property in question, and range from 50-400 km sˉ¹. In general, uniform rotation decreases both the frequencies and the large separations, but produces increases in the small separations. In differentially rotating models, the frequencies may either increase or decrease, depending on the mode. Since these variations move in opposite directions, it may be possible to constrain the interior angular momentum distribution from stellar pulsations. Unfortunately, the differences are small, and the observational challenges may be insurmountable. Finally, I investigate how the distortion in the shape of the eigenfunction influences photometric mode identification techniques. Increasing rotation increases the variation in photometric mode identification as a function of inclination, with the result that it may be impossible to rule out certain modes.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".