Properties of massive stars in the Tarantula Nebula
Bibliographic record
Abstract
This thesis presents the quantitative analysis of massive O-type stars in the Tarantula Nebula of the Large Magellanic Cloud. The data have been collected in the context of VLT-FLAMES Tarantula Survey (VFTS), an ESO Large Programme that has obtained multi-epoch optical spectroscopy of 800 OB-type stars using the Very Large Telescope (VLT) in Chile. My thesis studies around 330 O-type stars in the VFTS. The first part of the thesis focusses on the stellar rotational properties of spectroscopic single and binary O-type stars in the Tarantula Nebula. The distribution of spin rates of massive stars is important because it is the fingerprint of their formation process, which it is not well understood, and an important ingredient for their evolution. In the second part of the thesis, the focus shifts to properties of O-type giants, bright giants, and supergiants in the VFTS. Using quantitative spectroscopy, we constrain the stellar and wind parameters, and the surface abundances of the objects in our sample. We then confront these observational constraints to the predictions of theories of the evolution of massive stars.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".