Turbulence-induced disc formation in strongly magnetized cloud cores
Bibliographic record
Abstract
We present collapse simulations of strongly magnetized, turbulent molecular cloud cores with masses ranging from 2.6 to 1000 M⊙ in order to study the influence of the initial conditions on the turbulence-induced disc formation mechanism proposed recently by Seifried et al. We find that Keplerian discs are formed in all cases independently of the core mass, the strength of turbulence or the presence of global rotation. The discs appear within a few kyr after the formation of the protostar, are 50–150 au in size, and have masses between 0.05 and a few 0.1 M⊙. During the formation of the discs the mass-to-flux ratio stays well below the critical value of 10 for Keplerian disc formation. Hence, flux-loss alone cannot explain the formation of Keplerian discs. The formation of rotationally supported discs at such early phases is rather due to the disordered magnetic field structure and due to turbulent motions in the surroundings of the discs, two effects lowering the classical magnetic braking efficiency. Binary systems occurring in the discs are mainly formed via the disc capturing mechanism rather than via disc fragmentation, which is largely suppressed by the presence of magnetic fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".