VARIABLE ACCRETION IN THE EMBEDDED PHASE OF STAR FORMATION
Bibliographic record
Abstract
Motivated by the recent detection of a large number of embedded young stellar objects (YSOs) with mass accretion rates that are inconsistent with the predictions of the standard model of inside-out collapse, we perform a series on numerical hydrodynamic simulations of the gravitational collapse of molecular cloud cores with various initial masses, rotation rates, and sizes. We focus on the early Class I stage of star formation when circumstellar disks are exposed to high rates of mass deposition from infalling envelopes. Our numerical modeling reproduces the large observed spread in accretion rates inferred for embedded YSOs in Perseus, Serpens, and Ophiuchus star-forming regions by Enoch et al., yielding 37%–75% of objects with "sub-Shu" accretion rates yr −1 and 1%–2% of objects with "super-Shu" accretion rates yr −1 . Mass accretion rates in the Class I stage have a lognormal distribution, with its shape controlled by disk viscosity and disk temperature. The spread in is greater in models with lower viscosity and smaller in models with higher viscosity and higher disk temperature, suggesting that gravitational instability may be a dominant cause of the observed diversity in in embedded YSOs. Our modeling predicts a weak dependence between the mean mass accretion rates and stellar masses in the Class I stage, in sharp contrast to the corresponding steep dependence for evolved T Tauri stars and brown dwarfs.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".