The rapid rotation and complex magnetic field geometry of Vega<i>(Corrigendum)</i>
Bibliographic record
Abstract
New ZDI models have been recently computed for Vega, using the data sets presented by Petit et al. (2010). Stokes I pseudo-line profiles produced by the model were again compared to observed LSD pseudo-line profiles, and it was found that the model used by Petit et al. assumed a profile depth that was about 40% too large. All other parameters of the ZDI model were checked as well, and no other discrepancies were found. Several series of LSD profiles (computed from different line masks) were used by Petit et al. (2010), and the mismatch is most likely because the published ZDI model was calculated with a line depth optimized for a different set of LSD profiles with slightly different depths for normalization. Reconstructing new maps of the magnetic field of Vega from the 2008 and 2009 datasets using the corrected line profile model (Fig. 1), we found that the photospheric field strength is about 40% greater than previously claimed, with a peak field strength of about 7 G. We emphasize that the modified line depth changes only the field strength, without affecting other aspects of the magnetic geometry at all, i.e. the reconstructed orientation of the local magnetic field vector. All other conclusions of the paper regarding the period search, the photospheric field distribution, and the absence of detectable magnetic variability of Vega remain unchanged.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".