The Interaction Between Multi-Dimensional Convection and Radial Stellar Pulsation
Bibliographic record
Abstract
vi 4.2 Peak kinetic energy and period for OPAL opacities, a mass of M=0.575M ⊙ , and with the radial pulsation initiated at 2 km s -1 . . . . . . . . . . .4.3 Peak kinetic energy and period for KING opacities, a mass of M=0.7M ⊙ , and with the radial pulsation initiated at 2 km s -1 . . . . . . . . . . .4.4 Peak kinetic energy and period for OPAL opacities, a mass of M=0.7M ⊙ , and with the radial pulsation initiated at 2 km s -1 . . . . . . . . . . .4.5 Peak kinetic energy and period for OPAL opacities, a mass of M=0.7M ⊙ , and with the radial pulsation initiated at 10 km s -1 . . . . . . . . . . .5.1 Convective time dependence of 6300 K effective temperature model. .5.2 Convective time dependence of 6700 K effective temperature model. .5.3 Maximum convective flux amplitude dependence. . . . . . . . . . . .5.4 Comparison of 1D and 2D peak kinetic energy curves . . . . . . . . .5.5 6300 K effective temperature model's light curve compared to variable star v120's light curve. . . . . . . . . . . . . . . . . . . . . . . . . . .5.6 6300 K effective temperature model's light curve compared to variable star v19's light curve. . . . . . . . . . . . . . . . . . . . . . . . . . . .5.7 6400 K effective temperature model's light curve compared to variable star v48's light curve. . . . . . . . . . . . . . . . . . . . . . . . . . . .5.8 6500 K effective temperature model's light curve compared to variable star v93's light curve. . . . . . . . . . . . . .
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".