Self‐Correlation Analysis of RV Tauri Stars and Related Objects
Bibliographic record
Abstract
We have used self‐correlation—a simple form of variogram analysis—to study 33 RV Tauri and related stars in the LMC, using MACHO data. We confirm the periods and classifications of Alcock et al. and discuss a few stars of special interest. We find that self‐correlation is a useful adjunct to Fourier analysis, especially for stars whose classification is based on their cycle‐to‐cycle behavior. In particular, it can identify stars whose behavior is more complicated than the standard "alternating deep and shallow minima" and begin to investigate the question of whether the Population II Cepheids, the RV Tauri variables, and the SRd variables form a continuous sequence from periodicity to irregularity. Our results also emphasize that the RV Tauri phenomenon has two dimensions: the relative depths of adjacent minima and the number of cycles over which the alternating minima persist.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".