Bibliographic record
Abstract
Abstract The internal rotation and magnetism of massive stars are considered in response to (i) the inward pumping of angular momentum through deep and slowly rotating convective layers, and (ii) the winding up of a helical magnetic field in radiative layers. Field winding can transport angular momentum effectively even when the toroidal field is limited by kinking. Magnetic helicity is pumped into a growing radiative layer from an adjacent convective envelope (or core). The receding convective envelope that forms during the early accretion phase of a massive star is the dominant source of helicity in its core, yielding a 1012 − 1013 G polar magnetic field in a collapsed neutron star (NS) remnant. Using MESA models of various masses, we find that the NS rotation varies significantly, from P NS ∼ 0.1–1 s in a 13 M ⊙ model to P NS ∼ 2 ms in a 25 M ⊙ model with an extended convective core. Stronger inward pumping of angular momentum is found in more massive stars, due to the growing thickness of the convective shells that form during the later stages of thermonuclear burning. On the other hand, stars that lose enough mass to form blue supergiants in isolation end up as very slow rotators. The tidal spin-up of a 40 M ⊙ star by a massive binary companion is found to dramatically increase the spin of the remnant black hole, allowing a rotationally supported torus to form during the collapse. The implications for post-collapse decay or amplification of the magnetic field are also considered.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".