Bibliographic record
Abstract
In Kato et al. (2019, arXiv:1909.00910), I reported on a double outburst and rebrightenings in 2018 in V544 Her. Such a phenomenon is usually observed in WZ Sge stars which evolved after the period bounce and the colors of V544 Her in quiescence apparently exclude this possibility. Although this phenomenon was considered to be rare, I detected almost exactly the same one in 2021 using ZTF, ATLAS and ASAS-SN public data. I also detected a phenomenon very similar to this in ASASSN-19yt in 2022. The same object showed a different type of outburst in 2019 whose morphology looked like that of an SS Cyg star. If ASASSN-19yt is an SU UMa star, the morphology of the 2019 outburst would challenge our knowledge in SU UMa stars. If this object, or V544 Her, is an SS Cyg star, what causes a double outburst and rebrightenings would become an unsolved problem in dwarf novae.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".