Core formation via filament fragmentation and the impact of ambient pressure on it
Bibliographic record
Abstract
ABSTRACT Prestellar cores are generally spheroidal, some of which appear oblate while others appear prolate. Very few of them appear circular in projection. Little, however, is understood about the processes or the physical conditions under which prolate/oblate cores form. We find that an initially subcritical filament experiencing relatively low pressure (≲104 K cm−3) forms prolate cores (i.e. those with axial ratios in excess of unity) via gradual accumulation of gas in density crests. Meanwhile, a filament that is initially transcritical and experiences pressure similar to that in the Solar neighbourhood (between few × 104 and few × 105 K cm−3) forms oblate cores (i.e. those with axial ratios less than unity) via Jeans-like fragmentation. At higher pressure, however, fragments within the filament do not tend to survive as they rebound soon after formation. We also argue that quasi-oscillatory features of velocity gradient observed along the filament axis, and in the direction orthogonal to the axis, are integral to the filament evolution process and arise due to the growth of corrugations on its surface. The axial component of the velocity gradient, in particular, traces the gas flow along the filament length. We therefore posit that it could be used to constrain the filament-formation mechanism. The magnitude of the respective components of velocity gradients increases with increasing external pressure.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".