Collapse in self-gravitating turbulent fluids
Bibliographic record
Abstract
Motivated by the non-linear star formation efficiency found in recent numerical simulations by a number of workers, we perform high-resolution adaptive mesh refinement simulations of star formation in self-gravitating turbulently driven gas. As we follow the collapse of this gas, we find that the character of the flow changes at two radii, the disc radius rd and the radius r*, where the enclosed gas mass exceeds the stellar mass. Accretion starts at large scales and works inwards. In line with recent analytical work, we find that the density evolves to a fixed attractor, ρ(r, t) → ρ(r), for rd < r < r*; mass flows through this structure on to a sporadically gravitationally unstable disc and from thence on to the star. In the bulk of the simulation box, we find that the random motions vT ∼ rp with p ∼ 0.5 are in agreement with Larson's size–linewidth relation. In the vicinity of massive star-forming regions, we find p ∼ 0.2–0.3, as seen in observations. For r < r*, vT increases inwards, with p = −1/2. Finally, we find that the total stellar mass M*(t) ∼ t2 is in line with previous numerical and analytic work that suggests a non-linear rate of star formation.
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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 | 0.002 |
| 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 | 0.000 |
| 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".