Is the dust-to-gas ratio constant in molecular clouds?
Bibliographic record
Abstract
Abstract We perform numerical simulations of dusty, supersonic turbulence in molecular clouds. We model 0.1, 1 and 10 μm sized dust grains at an initial dust-to-gas mass ratio of 1:100, solving the equations of combined gas and dust dynamics where the dust is coupled to the gas through a drag term. We show that, for 0.1 and 1 μm grains, the dust-to-gas ratio deviates by typically 10–20 per cent from the mean, since the stopping time of the dust due to the gas drag is short compared to the dynamical time. Contrary to previous findings, we find no evidence for orders of magnitude fluctuation in the dust-to-gas ratio for ∼0.1 μm grains. Larger, 10 μm dust grains may have dust-to-gas ratios increased by up to an order of magnitude locally. Both small (0.1 μm) and large (≳1 μm) grains trace the large-scale morphology of the gas; however, we find evidence for ‘size-sorting’ of grains, where turbulence preferentially concentrates larger grains into dense regions. Size-sorting may help to explain observations of ‘coreshine’ from dark clouds and why extinction laws differ along lines of sight through molecular clouds in the Milky Way compared to the diffuse interstellar medium.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".