Stellar Model Analysis of the Oscillation Spectrum of η Bootis Obtained from<i>MOST</i>
Bibliographic record
Abstract
Eight consecutive low-frequency radial p -modes are identified in the G0 IV star η Bootis based on 27 days of ultraprecise rapid photometry obtained by the MOST ( Microvariability and Oscillations of Stars ) satellite. The MOST data extend smoothly, to lower overtones, the sequence of radial p -modes reported in earlier ground-based spectroscopy by other groups. The sampling is nearly continuous; hence, the ambiguities in p -mode identifications due to aliases, such as the cycle day -1 alias found in ground observations, are not an issue. The lower overtone modes from the MOST data constrain the interior structure of the model of η Boo, giving a best fit on a grid of ~300,000 stellar models for a composition of ( X , Z ) = (0.71,0.04), a mass of M = 1.71 ± 0.05 M ☉ , and an age of t = 2.40 ± 0.03 Gyr. The surface temperature and luminosity of this model, which were constrained only by using the oscillation modes, are close (1 σ) to current best estimates of η Boo's surface temperature and luminosity. With the interior fit anchored by the lower overtone modes seen by MOST , standard models are not able to fit the higher overtone modes with the same level of accuracy. The discrepancy, model minus observed frequency, increases from 0.5 μHz at 250 μHz to 5 μHz at 1000 μHz and is similar to the discrepancy that exists between the Sun's observed p -mode frequencies and the p -mode frequencies of the standard solar model. This discrepancy promises to be a powerful constraint on models of three-dimensional convection.
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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 | 0.000 |
| 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 | 0.000 |
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".