On the formation of cores in accreting filaments and the impact of ambient environment on it
Bibliographic record
Abstract
Abstract Results from some recent numerical works, including ours, lend credence to the thesis that the ambient environment, that is, the magnitude of external pressure, affects the star-forming ability of clouds and filaments. In continuation with our series of papers on this subject, we explore this thesis further by developing new hydrodynamic simulations of accreting filaments confined by external pressures in the range $10^{4 -7}$ K cm $^{-3}$ . Our principal findings are – (i) irrespective of linemass, filament-fragmentation generally yields spheroidal cores. The initially sub-critical filaments in low to intermediate external pressure environments form broad cores suggesting that weakly self-gravitating filaments must fragment via the collect – and- collapse mode to form broad cores. Transcritical filaments, by contrast, become susceptible to the Jeans-type instability and form pinched cores; (ii) the ambient environment bears upon the physical properties of filaments including their FWHM $_{fil}$ . Only the filaments initially suffused with subsonic turbulence in Solar-Neighbourhood-like environments, however, have FWHM $_{fil}$ $\sim$ 0.1 pc. In high pressure environs such filaments not only have much smaller widths, but also become severely eviscerated. On the contrary, filaments suffused with initially supersonic turbulence are typically broader; (iii) the quasi-oscillatory nature of velocity gradients must be ubiquitous along filament lengths and its magnitude generally increases with increasing pressure. The periodicity of the velocity gradients approximately matches the fragmentation lengthscale of filaments; (iv) oscillatory features of the radial component of the velocity gradient are a unreliable proxy for detecting signatures of accretion onto filaments; and (v) filaments at either extreme of external pressure are inefficient at cycling gas into the dense phase which could reconcile the corresponding inefficiency of star-formation in such environments.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".