A <i>HST</i> imaging survey of a sample of 61 Galactic Wolf-Rayet stars — the WC8-9 subsample
Bibliographic record
Abstract
A HST-wfpc2 survey of Galactic Wolf-Rayet stars was undertaken over a five year period, in an effort to discover new close visual companions, tight clusters, and/or association memberships. In total, 61 Galactic WR stars were observed, with nine objects being members of the subclasses WC8 and WC9, which are associated with dust production. For these nine, we present images of WR 11, WR 48a, WR 69, WR 70, WR 81, and WR 92. We refer to Wallace et al. (2002) for discussion of WR 98a, WR 104, and WR 112. Overall, we find for separations of approximately ≥ 150 mas, that the binary/association properties of the WC8/WC9 sample are statistically indistinguishable from the overall WR population. These statistics are limited, however, by the small numbers of each WR subclass observed.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".