Relation between Luminosity and Surface Rotation of Spotted Stars
Bibliographic record
Abstract
This communications explores the existence of a possible relationship between Luminosity and surface rotation for the study of the evolution of spotted stars using the data of Kepler’s and DI spotted stars. For the determination of such a relationship between luminosity and rotation the dependency of rotational shear on effective temperature is to be reviewed first. The strong dependence of rotational shear on the effective temperature in the range of 3000K and 6000K is confirmed by a power law. This dependence in turn introduces rotation as an evolutionary parameter for the study of the evolution of spotted stars. Multivariate Linear regression, Log-Log multivariate and Nonlinear Multivariate (2, 2) Degree models are constructed to determine the Luminosity of Kepler’s and Doppler imaging spotted stars with rotational shear, relative differential rotation and radius as independent variables. In this regard Log-Log model and Nonlinear Multivariate (2, 2) Degree model is best suited as compared to the linear model. In the next stage Log-Log model is applied to the main sequence Kepler’s stars (excluding giants) and also to the stars in the individual spectral classes A, F, G, K, and M. The model appears best for main sequence stars and also for the stars in the individual classes F-M. Applying the model on DI spotted stars the standard errors indicate that the adequacy of the model for DI spotted stars data is weak. A description of stellar motions and description of data and model used is given in the introduction.
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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 | 0.003 |
| 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".