Bibliographic record
Abstract
In the last five years our knowledge of magnetism in early-type stars has significantly improved because of the new gen- eration of high-resolution spectropolarimeters (ESPaDOnS@CFHT, Narval@TBL, HARPSpol@ESO). The success of the Magnetism in Massive Stars (MiMeS) Project, for example, has greatly improved our understanding of the magnetic properties of massive early-type stars; however, it was mainly focused on single stars. Summarized here is our general understanding of stellar magnetism in early-type stars and the recent findings from the MiMeS project. As recent results suggest a significant fraction of early-type stars are in binaries, this implies that the interplay between magnetic fields and binarity has yet to be investigated in any significant detail. To this end, the current analysis of the recently discovered magnetic field in the rapidly-rotating secondary star of the close, massive binary system known as Plaskett’s star – a unique system that is testing our current theories of binarity and magnetism in early-type stars, and a target for investigation in the recently accepted Binarity and Magnetic Interactions in various classes of Stars (BinaMIcS) Large Programs at CFHT and TBL – is discussed.
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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 | 0.001 | 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) | 0.000 | 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".