Cluster-formation in the Rosette molecular cloud at the junctions of filaments
Bibliographic record
Abstract
For many years feedback processes generated by OB-stars in molecular clouds, including expanding ionization fronts, stellar winds, or UV-radiation, have been proposed to trigger subsequent star formation. However, hydrodynamic models including radiation and gravity show that UV-illumination has little or no impact on the global dynamical evolution of the cloud. The Rosette molecular cloud, irradiated by the NGC2244 cluster, is a template region for triggered star-formation, and we investigated its spatial and density structure by applying a curvelet analysis, a filament-tracing algorithm (DisPerSE), and probability density functions (PDFs) on Herschel column density maps, obtained within the HOBYS key program. The analysis reveals not only the filamentary structure of the cloud but also that all known infrared clusters except one lie at junctions of filaments, as predicted by turbulence simulations. The PDFs of sub-regions in the cloud show systematic differences. The two UV-exposed regions have a double-peaked PDF we interprete as caused by shock compression. The deviations of the PDF from the log-normal shape typically associated with low- and high-mass star-forming regions at Av~3-4m and 8-10m, respectively, are found here within the very same cloud. This shows that there is no fundamental difference in the density structure of low- and high-mass star-forming regions. We conclude that star-formation in Rosette - and probably in high-mass star-forming clouds in general - is not globally triggered by the impact of UV-radiation. Moreover, star formation takes place in filaments that arose from the primordial turbulent structure built up during the formation of the cloud. Clusters form at filament mergers, but star formation can be locally induced in the direct interaction zone between an expanding HII--region and the molecular cloud.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".