Bibliographic record
Abstract
Introductory background The eclipsing binaries and spotted stars discussed in the previous three chapters still represent only about a quarter of all variables. The largest class of variable stars are those having some inherent physical variation in luminosity, as distinct from an effect of geometry. They are often referred to as pulsating, sometimes vibrating, stars: words suggesting the physical cause of the variation. Although, as noted before, all stars would vary over a sufficiently long timescale, an appreciable intrinsic variation of luminosity accessible to human inspection implies a very short period against general stellar time frames, giving perspective to such terminology. There are examples whose light pattern repeats in measurably the same form for many cycles, with a periodicity of comparable constancy to that of eclipsing variables. Others show varying degrees of chaotic behaviour. In ‘irregular’ cases, the light level wanders up and down with no pattern or predictability. But many show quasi-periodic variations of a ‘semiregular’ nature. Some examples of the different light curves are shown in Figure 11.1. Spectroscopy shows that the variations in apparent magnitude are linked with changes of radius. By studying the Doppler shifts of absorption lines in the regular cepheid type variables (prototype δ Cephei), it was deduced that the star oscillates inward and outward in the same period as the brightness cycle, the star being faintest not far from, but somewhat before, the time when it is smallest.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.043 |
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".