Investigation of the magnetic field characteristics of Herbig Ae/Be\n stars: Discovery of the pre-main sequence progenitors of the magnetic Ap/Bp\n stars
Bibliographic record
Abstract
We are investigating the magnetic characteristics of pre-main sequence Herbig\nAe/Be stars, with the aim of (1) understanding the origin and evolution of\nmagnetism in intermediate-mass stars, and (2) exploring the influence of\nmagnetic fields on accretion, rotation and mass-loss at the early stages of\nevolution of A, B and O stars. We have begun by conducting 2 large surveys of\nHerbig Ae/Be stars, searching for direct evidence of photospheric magnetic\nfields via the longitudinal Zeeman effect. From observations obtained using\nFORS1 at the ESO-VLT and ESPaDOnS at the Canada-France-Hawaii Telescope, we\nreport the confirmed detection of magnetic fields in 4 pre-main sequence A- and\nB-type stars, and the apparent (but as yet unconfirmed) detection of fields in\n2 other such stars. We do not confirm the detection of magnetic fields in\nseveral stars reported by other authors to be magnetic: HD 139614, HD 144432 or\nHD 31649. One of the most evolved stars in the detected sample, HD 72106A,\nshows clear evidence of strong photospheric chemical peculiarity, whereas many\nof the other (less evolved) stars do not. The magnetic fields that we detect\nappear to have surface intensities of order 1 kG, seem to be structured on\nglobal scales, and appear in about 10% of the stars studied. Based on these\nproperties, these magnetic stars appear to be pre-main sequence progenitors of\nthe magnetic Ap/Bp stars.\n
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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) | 0.001 | 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".