Filament Accretion and Fragmentation in the Perseus Molecular Cloud
Bibliographic record
Abstract
Abstract Observations suggest that filaments in molecular clouds can grow by mass accretion while forming cores via fragmentation. Here, we present one of the first large-sample studies of filament accretion using velocity gradient measurements of star-forming filaments on the ∼0.05 pc scale with NH3 observations of the Perseus Molecular Cloud, primarily obtained as a part of the Green Bank Ammonia Survey. In this study, we find significant correlations between the velocity gradient, velocity dispersion, mass per unit length, and number of cores per unit length of the Perseus filaments. Our results suggest a scenario in which filaments not only grow through mass accretion, but also form new cores continuously in the process, well into the thermally supercritical regime. Such behavior is contrary to that expected from isolated filament models but consistent with how filaments form within a more realistic cloud environment, suggesting that the cloud environment plays a crucial role in shaping core formation and evolution in filaments. Furthermore, even though velocity gradients within filaments are not oriented randomly, we find no correlation between velocity gradient orientation and the filament properties we analyzed. This result suggests that gravity is unlikely to be the dominant mechanism imposing order on the ∼0.05 pc scale for dense star-forming gas.
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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) | 0.000 | 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".