Bibliographic record
Abstract
In this thesis I present a set of scaling relationships for ZAMS rotating models. These relationships are applicable to models whose rotation rate produces a given surface distortion and are shown to be valid for stellar masses from 1.875 M[circle dot] to 2.5 M[circle dot]. Some of the scaling relationships are used to determine intrinsic stellar properties such as the real luminosity and effective temperature of the star based on deduced properties from interferometry and other observations, providing a direct way to place a rotating star into the HR diagram. I also present pulsation calculations of p and g-modes for a set of these rotating models using a 2D linear adiabatic code. I computed and classified more than ten thousand pulsation modes and found scaling relationships for both the p-modes and g-modes between models of different masses with the same surface shape. The p-mode scaling is well-described by the root-mean density relation but the scaling of g-modes is found to depend on properties outside of the convective core. These pulsation mode scaling properties could be useful for selecting optimal stellar models to match the observed oscillation frequencies of individual stars. Finally, I considered the observability of the modeled oscillation modes by perturbing the surface properties of static rotating models and calculating the effect each mode would have on the observed magnitude of the star at different inclinations. The results suggest that there are some modes that could be ruled out from the analysis when comparing the theoretical and observed oscillation spectra of a rotating star for the rotation rates considered.
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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 | 0.001 | 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) | 0.002 | 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".