Dust motion and possibility of dust growth in a growing circumstellar disk
Bibliographic record
Abstract
ABSTRACT We calculate the evolution of a star-forming cloud core using a three-dimensional resistive magnetohydrodynamics simulation, treating dust grains as Lagrangian particles, to investigate the dust motion in the early star formation stage. We prepare six different-sized set of dust particles in the range ad = 0.01–$1000\, \mu$m, where ad is the dust grain size. In a gravitationally collapsing cloud, a circumstellar disk forms around a protostar and drives a protostellar outflow. Almost all the small dust grains (ad ≲ 10–$100\, \mu$m) initially distributed in the region θ0 ≲ 45° are ejected from the center by the outflow, where θ0 is the initial zenith angle relative to the rotation axis, whereas only a small number of the large dust grains ($a_{\rm d} \gtrsim 100\, \mu$m) distributed in the region are ejected. All other grains fall onto either the protostar or disk without being ejected by the outflow. Regardless of the dust grain size, the behavior of the dust motion is divided into two trends after dust particles settle into the circumstellar disk. The dust grains reaching the inner disk region from the upper envelope preferentially fall onto the protostar, while those reaching the outer disk region or disk outer edge from the envelope can survive without an inward radial drift. These surviving grains can induce dust growth. Thus, we expect that the outer disk regions could be a favored place of planet 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".