An ALMA study of outflow parameters of protoclusters: outflow feedback to maintain the turbulence
Bibliographic record
Abstract
ABSTRACT With the aim of understanding the role of outflows in star formation, we performed a statistical study of the physical parameters of outflows in 11 massive protoclusters associated with ultracompact H ii regions. A total of 106 outflow lobes are identified in these protoclusters using the ALMA CO (3–2), HCN (4–3), and HCO+ (4–3) line observations. Although the position angles of outflow lobes do not differ in these three tracers, HCN and HCO+ tend to detect lower terminal velocity of the identified outflows compared to CO. The majority of the outflows in our targets are young with typical dynamical time-scales of 102–104 yr, and are mostly composed of low-mass outflows along with at least one high-mass outflow in each target. An anticorrelation of outflow rate with dynamical time-scale indicates that the outflow rate possibly decreases with time. Also, a rising trend of dynamical time-scale with the mass of the associated core hints that the massive cores might have longer accretion histories than the low-mass cores. Estimation of different energies in these protoclusters shows that outflows studied here cannot account for the generation of the observed turbulence, but can sustain the turbulence at the current epoch as the energy injection rate from the outflows is similar to the estimated dissipation rate.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".