FORMATION OF COLLAPSING CORES IN SUBCRITICAL MAGNETIC CLOUDS: THREE-DIMENSIONAL MAGNETOHYDRODYNAMIC SIMULATIONS WITH AMBIPOLAR DIFFUSION
Bibliographic record
Abstract
We employ three-dimensional magnetohydrodynamic simulations including ambipolar diffusion to study the gravitationally driven fragmentation of subcritical molecular clouds, in which the gravitational fragmentation is stabilized as long as magnetic flux-freezing applies. The simulations show that the cores in an initially subcritical cloud generally develop gradually over an ambipolar diffusion time, which is about a few ×10 7 yr in a typical molecular cloud. On the other hand, the formation of collapsing cores in subcritical clouds is accelerated by supersonic nonlinear flows. Our parameter study demonstrates that core formation occurs faster as the strength of the initial flow speed in the cloud increases. We found that the core formation time is roughly proportional to the inverse of the square root of the enhanced density created by the supersonic nonlinear flows. The density dependence is similar to that derived in quasistatically contracting magnetically supported clouds, although the core formation conditions are created by the nonlinear flows in our simulations. We have also found that the accelerated formation time is not strongly dependent on the initial strength of the magnetic field if the cloud is highly subcritical. Our simulation shows that the core formation time in our model subcritical clouds is several ×10 6 yr due to the presence of large-scale supersonic flows (∼3 times sound speed). Once a collapsing core forms, the density, velocity, and magnetic field structure of the core do not strongly depend on the initial strength of the velocity fluctuation. The infall velocities of the cores are subsonic and the magnetic field lines show weak hourglass shapes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".