**FULL TITLE** ASP Conference Series, Vol. **VOLUME**, **YEAR OF PUBLICATION** **NAMES OF EDITORS** Magnetic topologies of cool stars
Bibliographic record
Abstract
Abstract. Stellar magnetic fields can be investigated using several, very complementary approaches. While conventional spectroscopy is capable of estimating the average magnetic strength of potentially complex field configurations thanks to its low sensitivity to the vector properties of the field, spectropolarimetry can be used to map the medium- and large-scale structure of magnetic topologies. In particular, the latter approach allows one to retrieve information about the poloidal and toroidal components of the large-scale dynamo fields in low-mass stars, and thus to investigate the physical processes that produce them. Similarly, this technique can be used to explore how magnetic fields couple young stars to their massive accretion disc and thus to estimate how much mass and angular momentum are transfered to the newly-born low-mass star. We present here the latest results in this field obtained with spectropolarimetry, with special emphasis on the surprising discoveries obtained on very-low mass fully-convective stars and classical T Tauri stars thanks to the ESPaDOnS spectropolarimeter recently installed on the 3.6m Canada-France-Hawaii Telescope.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.232 | 0.180 |
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".